マテリアルズ・インフォマティクス関連情報 [総合索
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マテリアルズ・インフォマティクス(MI)関連情報。各情報の中には的確でない、間違いや誤った記述があるかもしれません。ご注意下さい(→著作権その他の諸注意、免責説明[ページ])。(メイルでの御指摘大歓迎です)
- Materials Informatics
- Materials Informatics、マテリアルズ・インフォマティクス:参考ページ[1]Materials Project、[2]Materials Evolution(アクセス不能)、[3]AFLOWLIB.ORG: a distributed materials genome properties repository from high-throughput ab-initio calculation.、[4]Center for Inverse Design(*) 、[5]Center for Hierarchical Materials Design(*)(ChiMaD)、[6]スパースモデリングの深化と高次元データ駆動科学の創成(文科省科研費「新学術領域研究」)[7]NoMaD(Novel Materials Discovery Repository)[8]統合型材料開発・情報基盤部門(MaDIS)【関連語】Inverse materials design, Materials genome, Data mining, Data mapping, Big Data, Database、ビッグデータ、データベース、機械学習、重回帰分析、多変量解析、エキスパートシステム、可視化
[NIMS関連プレスリリース情報(含む"NIMS NOW")][小目次]
[17]「Artificial Intelligence Learns to Predict Photo-Functional Molecules」、詳細はNIMS-MANAのResearch Highlights(Vol. 45)参照。
[18]「New Material That is Both a Thermoelectric and a Superconductor Identified by High-Throughput Materials Discovery」、詳細はNIMS-MANAのResearch Highlights(Vol. 46)参照。
[19]「機械学習により世界最高クラスの熱放射多層膜を設計し、その実証に成功 〜 約80億の候補から最適構造を探索 省エネルギー社会への貢献に期待 〜」、詳細はNIMSのプレスリリース参照。
[20]「機械可読性を高める計測データのメタ情報抽出ツール(M-DaC)の開発と一般提供 〜装置やメーカーで異なるデータ形式を統一 データ科学による新材料開発の促進に期待 〜」、詳細はNIMSのプレスリリース参照。
[21]「機械学習の「記憶」を活用し、高分子の熱伝導性の大幅な向上に成功 〜少ないデータでも高精度な予測が可能に 高分子での材料インフォマティクス加速に期待〜」、詳細はNIMSのプレスリリース参照。
[22]「物性予測タスク訓練済みモデルの包括的ライブラリXenonPy.MDLを公開 〜転移学習で材料インフォマティクスのスモールデータの壁を乗り越える〜」、詳細はNIMSのプレスリリース参照。
[23]「機械学習により結晶粒界の熱伝導度を局所原子配列から高精度に予測 〜計算科学と粒界ナノ構造に基づく新たな材料開発指針〜」、詳細はNIMSのプレスリリース参照。
[24]「「例外」を発見するAI「BLOX」の開発 - AIを用いた革新材料の開発に新たな道筋 - 」、詳細はNIMSのプレスリリース参照。
[25]「材料データの利活用に貢献する「Materials Data Repository (MDR)」とNIMSが発信する材料データプラットフォームDICE (ダイス)のWebサイトの公開 〜材料データの収集・蓄積・公開、データ駆動型研究の促進へ向けて〜」、詳細はNIMSのニュース記事参照。
[26]「機械学習により薄膜作製プロセスの高速化を実現 〜外部データなしで試料作製回数を大幅に低減、材料開発コスト削減に期待〜」、詳細はNIMSのプレスリリース参照。
[27]「機械学習により超合金粉末の製造コスト削減に成功 〜数回の試行で複雑な製造条件を最適化 航空機エンジン部品製造の低コスト化に期待〜」、詳細はNIMSのプレスリリース参照。
[28]「SIP研究成果を社会実装するためのマテリアルズインテグレーションコンソーシアム発足」、詳細はNIMSのプレスリリース参照。当該サイト:MIコンソーシアム
[29]「最少の実験回数で高い予測精度を与える汎用的AI技術を開発 〜材料開発のDX : NIMS、旭化成、三菱ケミカル、三井化学、住友化学の水平連携で実現〜」、詳細はNIMSのプレスリリース参照。
[30]「人工ニューラルネットワークで明らかになった高温超伝導の隠れた起源」、詳細はNIMSのプレスリリース参照。
[31]「機械学習を活用した効率的なネオジム磁石の高特性化に成功 〜限られた実験データから最小限の実験でネオジム磁石の最適な作製条件を予測〜」、詳細はNIMSのプレスリリース参照。
[32]「データ駆動型電極触媒解析アルゴリズムの開発 〜人の“気付き”を支援することで脱炭素社会実現のための効率的な電極触媒材料探索への道筋〜」、詳細はNIMSのプレスリリース参照。
[33]「人工知能で蛍光有機分子を開発 - 複雑な現象を示す機能性分子の開発に貢献 - 」、詳細はNIMSのプレスリリース参照。
[34]「自動実験ロボットとデータ科学の連携により - リチウム空気電池のサイクル寿命を向上する電解液の開発に成功 - 」、詳細はNIMSのプレスリリース参照。
[35]「データ科学でハッキリ見えた微生物発電 - 微生物燃料電池や生分解性材料のデータ駆動研究に向けて - 」、詳細はNIMSのプレスリリース参照。
[36]「自律自動実験のための汎用ソフトフェア : NIMS-OSを開発 - ロボット実験と材料探索用AIの連携プラットフォーム - 」、詳細はNIMSのプレスリリース参照。
[37]「AIと材料研究者のコラボで耐熱材料を強くする - AIの一見奇抜な「手」から納得の熱処理法を考案 - 」、詳細はNIMSのプレスリリース参照。
[38]「銅合金の特性予測モデルを構築 - 三菱マテリアルのマグネシウム銅合金「MSPシリーズ」優位性を裏付け - 」、詳細はNIMSのプレスリリース参照。
[39]「進化するAIがエコな水素の普及のための新規材料開発を支援する - 白金族元素を使わない電極材料を探し出す - 」、詳細はNIMSのプレスリリース参照。
[公募等”募集”情報][小目次]
[採用情報](12/1、2017から期限のないものは原則掲載しません)[小目次]
[セミナー情報(含む交流会、講習会、コロキウム)][小目次]
[82]第43回MaDIS研究交流会:電子情報通信学会サービスコンピューティング研究会・MaDIS交流会合同研究会、5月31日、6月1日(2019)、開催地:物質・材料研究機構、千現地区、研究本館第一会議室、第二会議室(つくば)、詳細は案内ページ参照[終了]。
[83]2019年度MI2Iマテリアルズ・インフォマティクスハンズオンセミナー実践スキル(XenonPy)、講師:吉田亮(統計数理研究所)、劉暢(統計数理研究所)、Stephen Wu(統計数理研究所)、野口揺(統計数理研究所)、6月13日(2019)、開催地:情報・システム研究機構 統計数理研究所 3階:D304(セミナー室2)(立川、東京)、詳細は案内ページ参照[終了]。
[84]2019年度MI2Iマテリアルズ・インフォマティクスハンズオンセミナー実践スキル(HomCloud)、講師:赤木和人(東北大学AIMR)、6月26日(コースA)[終了]、7月9日(コースB)(2019)[終了]、開催地:AIMR本館2階セミナー室(仙台、宮城)、詳細は案内ページ参照。
[85]第44回MaDIS研究交流会、講師:Randy Jalem氏(NIMS)、6月27日(2019)午後4時半より、開催地:物質・材料研究機構、並木地区、M-cube棟2階セミナー室(つくば)、詳細は案内ページ参照[終了]。
[86]2019年度MI2Iマテリアルズ・インフォマティクスハンズオンセミナー(入門Orange1日コース)、講師:木野日織(NIMS)、6月28日(2019)、開催地:科学技術振興機構 東京本部別館2F A2会議室(千代田区、東京)、詳細は案内ページ参照[終了]。
[87]第45回統合型材料開発・情報基盤研究交流会・一般公開シリーズ、講師:東後篤史(京都大学)、7月12日(2019)午前10時半より、開催地:物質・材料研究機構、並木地区、WPI-MANA棟 Auditorium(つくば)、詳細は案内ページ参照[終了]。
[88]第46回MaDIS研究交流会、10月29日(2019)、開催地:物質・材料研究機構 千現地区 先進構造材料研究棟5階(つくば、茨城)、詳細は案内ページ参照[終了]。
[89]第47回MaDIS研究交流会、12月9日(2019)、開催地:物質・材料研究機構 千現地区 研究本館居室棟1階 第2会議室(つくば、茨城)、詳細は案内ページ参照[終了]。
[90]2019年度MI2Iマテリアルズ・インフォマティクスハンズオンセミナー(中級1日コース):『Scikit-learnによるマテリアルズ・インフォマティクス基本スキル』、講師:木野日織(NIMS)、8月5日(2019)、開催地: 科学技術振興機構 東京本部別館2F A1,A2会議室(千代田区、東京)、詳細は案内ページ参照[終了]。
[91]2019年度MI2Iマテリアルズ・インフォマティクスハンズオンセミナー(初級2日コース):『よくわかるマテリアルズ・インフォマティクス基礎』 〜理解を重点に、講師:小山幸典(NIMS)、8月26日、27日(2019)、開催地:科学技術振興機構 東京本部別館2F A2会議室(千代田区、東京)、詳細は案内ページ参照[終了]。
[92]MI2Iチュートリアルセミナー(第10回)「マテリアルズ・インフォマティクスの基本原則と手順」、9月9日(2019)、開催地:科学技術振興機構(JST)東京本部別館1階ホール(千代田区、東京)、詳細は案内ページ参照[終了]。
[93]2019年度MI2Iマテリアルズ・インフォマティクスハンズオンセミナー実践スキル(XenonPy)転移学習編、9月25日(2019)、開催地:情報・システム研究機構 統計数理研究所3階セミナー室4(D312B)(立川市、東京)、詳細は案内ページ参照[終了]。
[94]2019年度MI2Iマテリアルズ・インフォマティクスハンズオンセミナー(上級1日コース):『Python によるマテリアルズ・インフォマティクス・スキルアップ』 〜scikit-learn以外も体験してみる、講師:木野日織(NIMS)、9月30日(2019)、開催地:東京、詳細は案内ページ参照[終了]。
[95]2019年度MI2Iマテリアルズ・インフォマティクスハンズオンセミナー実践スキル COMBO for Windows、10月17日(2019)、開催地:科学技術振興機構 東京本部別館2F A1, A2会議室(千代田区、東京)、詳細は案内ページ参照[終了]。
[96]表面科学セミナー2019(実践編):「実践! マテリアルインフォマティクス 実際の作業を通して身につける材料データ科学」、11月6日、7日(2019)、開催地:大阪大学豊中キャンパス 理学部本館E棟 E211講義室(豊中、大阪)、詳細は案内ページ参照[終了]。
[97]“TIAかけはし”ポスター交流会2019:「-計算と計測のデータ同化による革新的物質材料解析手法の調査-」、12月6日(2019)、開催地:東京大学柏の葉キャンパス駅前サテライ(柏、千葉)、詳細は案内ページ参照[終了]。
[98]表面科学セミナー2020(実践編):「実践! マテリアルインフォマティクス 実際の作業を通して身につける材料データ科学」、1月22日、23日(2020)、開催地:東京理科大学 森戸記念館 第2フォーラム(新宿区、東京)、詳細は案内ページ参照[終了]。
[99]知識科学系セミナー:「マテリアルズインフォマティクスの動向と課題 〜「できた」と「分かる」」、1月24日(2020)、開催地:北陸先端科学技術大学院大学 知識科学系講義棟 中講義室(能美市、石川)、詳細は案内ページ参照[終了]。
[100]第79回J-BEANSセミナー「コンピューターで新材料は設計できるか?」、3月30日(2020)、講演者:中野晃佑助教(先端科学技術研究科 情報科学系)、開催地:北陸先端科学技術大学院大学 ラーニング・コモンズ「J−BEANS」(大学会館1階)(石川県)、詳細は案内ページ参照[中止]。
[101]第9回MaSC技術交流会"Real Exchange":「計算科学の最前線 - 易しい基礎から航空機まで - 」、8月19日(2020)、Cisco WebEX によるオンライン開催、詳細は案内ページ参照[終了]。
[102]セミナー:「強磁性物質データベースの構築と磁気特性予測への応用」、12月14日(2020)午後3時半より、講演者:櫻井誠大氏(東大物性研)、開催地:Zoomによる開催、詳細は案内ページ参照[終了]。
[103]第1回オンラインサロン「スパコンコロキウム」:「データ同化による物質構造推定」、4月14日(2021)午後4時より、講演者:藤堂眞治氏(東大大学院理学系研究科物理学専攻)、開催地:Webexを使用したオンラインセミナー、詳細は案内ページ参照[終了]。
[104]マテリアルズ インフォマティクス総合ウェビナー(有償)「Whole view of MI」:「日本におけるMIの現状、これから、そしてMIを越えて」、5月27日、28日(2021)、参加申込締切:5月24日午後5時、開催地:オンライン開催、詳細は案内ページ参照[終了]。
[105]セミナー:「データ科学で加速する分子シミュレーション」、7月1日(2021)午後4時15分より、講演者:加藤幸一郎准教授(九州大学 大学院工学研究院)、開催地:Zoom*による開催、詳細は案内ページ参照[終了]。
[106]第39回エレクトロセラミックスセミナー、7月14日(2021)、開催地:オンライン開催、詳細は案内ページ参照[終了]。
[107]理論セミナー(物性研)、「Scalable Boltzmann machine learning by quantum annealer」、講演者:Prof. Masayuki Ohzeki(Graduate School of Information Sciences, Tohoku University/Institute for Innovative Research Tokyo Institute of Technology/Sigma-I Co. Ltd.)、7月30日(2021)午後4時より、開催地:オンライン開催(Zoom)、詳細は案内ページ参照[終了]。
[108]機能物性セミナー(物性研)、「マテリアルズインフォマティクスによる材料・触媒開発の実施例と方法」、講演者:高橋啓介氏(北海道大学)、9月24日(2021)午前10時より、開催地:オンライン開催、詳細は案内ページ参照[終了]。
[109]SCCJ講習会-機械学習講座、11月6日(2021)、開催地:オンライン開催、詳細は案内ページ参照[終了]。
[110]表面科学セミナー2022(実践編):「実践! マテリアルズインフォマティクス 実例を通じて学ぶマテリアルズインフォマティクス」、3月10日、11日(2022)、開催地:オンライン開催、詳細は案内ページ参照[終了]。
[111]CCMSハンズオン: PHYSBO講習会、6月20日(2022)、開催地:現地会場/東京大学物性研究所6階大講義室、オンライン会場/Web会議システムWebex(ハイブリッド開催)、詳細は案内ページ参照[終了]。
[112]【オンライン講義】「マテリアルズ・インフォマティクスの基礎と応用」(9月開講/全8回)、開催地:「Zoom」を利用したオンライン講義、詳細は案内ページ参照。
[113]第7回スパコンコロキウム:「データ科学による新物質の予測と発見」、講演者:吉田亮氏(統計数理研究所 ものづくりデータ科学研究センター)、10月6日(2022)午後4時より、開催地:Webexを使用したオンラインセミナー、詳細は案内ページ参照[終了]。
[114]強磁場コラボラトリーセミナー(物性研)、「機械学習を用いた実験データからの有効モデル推定」、講演者:田村亮先生(物質・材料研究機構)、10月28日(2022)午前11時より、開催地:オンライン開催、詳細は案内ページ参照[終了]。
[115]マテリアルズ・インフォマティクス チュートリアル連続セミナー、開催地:オンライン開催、詳細は案内ページ参照。
[116]データ連携部会セミナー(データ連携部会企画):「機械学習と嗅覚センサによるニオイ解析」、講演者:田村亮博士(物質・材料研究機構)、2月1日(2023)、開催地:オンライン開催(ZOOM)、詳細は案内ページ参照[終了]。
[117]理論セミナー:「第一原理電子状態計算による磁性材料のデータ駆動型探索」、講演者:深澤太郎氏(国立研究開発法人 産業技術総合研究所)、2月3日(2023)午後4時より、開催地:On Zoom and Lecture Room A632(物性研)(柏、千葉)(ハイブリッド開催)、詳細は案内ページ参照[終了]。
[118]ナノサイエンスセミナー:「Materials Data Repository(MDR)の開発と運用」「MDR XAFS DB 放射光データの統合と公開はいかにしてできたか」、講演者:田邉浩介氏(国立研究開発法人物質・材料研究機構)、石井真史氏(国立研究開発法人物質・材料研究機構)、2月27日(2023)午後3時より、開催地:Zoom開催、詳細は案内ページ参照[終了]。
[119]第48回ニューセラミックスセミナー:マテリアルズ・インフォマティクスによるものづくり - 基礎と材料・プロセス開発への応用 - 、2月28日(2023)、開催地:大阪産業創造館6階 会議室E(大阪)、詳細は案内ページ参照[終了]。
[120]表面科学セミナー2023(実践編) マテリアルインフォマティクスの基礎と情報科学を用いた実験データ解析、3月20日(2023)、開催地:大田区産業プラザPiO 特別会議室(東京)、詳細は案内ページ参照[終了]。
[121]「深化するデータ科学と表面科学」、3月28日(2023)、開催地:オンライン、詳細は案内ページ参照[終了]。
[122]2023年度 DxMT事例セミナー(第4回) :「機械学習による材料の予測・理解・発見:ソフトウェアと活用事例の紹介を中心に」、講演者:吉田亮氏(統計数理研究所)、11月29日(2023)午後2時より、開催地:オンライン開催、詳細は案内ページ参照[終了]。
[123]表面科学セミナー2024(実践編) 基礎と実用例を通じてこれから学ぶインフォマティクス、3月14日(2024)、開催地:大田区産業プラザPiO 特別会議室(東京) またはオンライン受講(ハイブリッド開催)、詳細は案内ページ参照[終了]。
[124]理論インフォーマルセミナー:「AI meets Theoretical Physics: machine learning assisted solution of a difficult problem in frustrated magnetism」、講演者:Prof. Nic Shannon(Okinawa Institute of Science and Technology)、6月10日(2024)午後4時より、開催地:物性研究所本館6階 第5セミナー室 (A615)(柏、千葉)、詳細は案内ページ参照[終了]。
[125]セミナー:「Machine Learning for Quantum Materials」、講演者:Prof. Eun-Ah Kim(Cornell University)、6月24日(2024)午後4時より、開催地:物性研究所本館6階 第5セミナー室 (A615)(柏、千葉)及びZoom(ハイブリッド開催)、詳細は案内ページ参照[終了]。
[126]【オンライン講義】「マテリアルズ・インフォマティクスの基礎と応用」(11月開講/全8回)、開催地:Zoomを利用したオンライン講義、詳細は案内ページ参照
[フォーラム情報][小目次]
[11]第8回MI2Iフォーラム、2月14日(2019)、開催地:吹上ホール メインホール(名古屋、愛知)、詳細は案内ページ参照[終了]。
[12]第9回MI2Iフォーラム、11月15日(2019)、開催地:大阪大学医学部学友会館・医療情報センター 銀杏会館(吹田市、大阪)、詳細は案内ページ参照[終了]。
[13]第4回MIRCフォーラム(PDF形式ページ) 〜環境・基盤マテリアルの新展開〜、10月24日(2022)、開催地:Zoomによるオンライン開催、詳細は案内ページ参照[終了]。
[14]2023年度学術討論会・技術交流フォーラム(PDF形式ページ):「データ駆動・創出・活用型マテリアル研究最前線」、2月8日(2024)、開催地:Zoomを用いたオンライン開催、詳細は案内ページ参照[終了]。
[研究会情報](含むシンポジウム、講演会)[小目次]
[41]分子科学研究所所長招聘会議:「化学の近未来:化学と情報科学との融合」、5月29日(2019)、開催地:岡崎コンファレンスセンター(岡崎、愛知)、詳細は案内ページ参照[終了]。
[42]第2回MI2I・JAIST合同シンポジウム(情報統合型物質・材料開発イニシアティブ・北陸先端科学技術大学院大学)「データ科学における予測と理解の両立を目指して - 複眼で見る - 」、6月5日(2019)、開催地:JST東京本部別館1階ホール(千代田区、東京)、詳細は案内ページ参照[終了]。
[43]第152回微小光学研究会「AIで拡げる微小光学」、6月14日(2019)、開催地:早稲田大学 西早稲田キャンパス 55号館N棟1階大会議室(新宿区、東京)、詳細は案内ページ参照[終了]。
[44]第151回結晶工学分科会研究会:「いまからはじめるインフォマティクス 〜チュートリアルから先端事例まで〜 」、6月17日(2019)、開催地:産総研臨海副都心センター別館(江東区、東京)、詳細は案内ページ参照[終了]。
[45]日本材料科学会第6回マテリアルズ・インフォマティクス基礎研究会(PDF形式ページ):「表面、ナノ構造におけるエネルギー材料開発と低次元物理現象」、8月30日(2019)、開催地:慶應義塾大学 矢上キャンパス 14棟203 号室(セミナールーム3)(横浜、神奈川)、詳細は案内ページ参照[終了]。
[46]CREST「ビッグデータ応用」シンポジウム、9月25日(2019)、開催地:アキバホール(富士ソフトアキバプラザ 5階)(千代田区、東京)、詳細は案内ページ参照[終了]。
[47]講演会「マテリアルズインフォマティクスを用いたものづくり最先端」、9月27日(2019)、開催地:ワイム貸会議室お茶の水(千代田区、東京)、詳細は案内ページ参照[終了]。
[48]MI2I最終報告会、2月19日、20日(2020)、開催地:一橋講堂 (千代田区、東京、〔学術総合センター内〕)、詳細は案内ページ参照[終了]。
[49]兵庫県マテリアルズ・インフォマティクス講演会:「データ駆動型材料科学の基礎と記述子設計技術」、8月26日(2020)、開催地:オンライン開催(Cisco Webexを利用)、詳細は案内ページ参照[終了]。
[50]講演会:「インフォマティクス技術の導入から産業応用まで〜高分子・機能性材料・バイオ・半導体」、9月25日(2020)、開催地:オンライン開催(zoom)に変更、詳細は案内ページ参照[終了]。
[51]応用電子物性分科会 研究例会:マテリアルズインフォマティクス 「〜 データ駆動型開発の導入、実践から応用まで 〜」、12月18日(2020)、開催地:Zoomによるオンライン開催、詳細は案内ページ参照[終了]。
[52]物性科学におけるデータ科学の今と未来、2月22日、24日(2021)、開催地:オンライン開催、参加申込期限:2月19日(2021)午後5時、詳細は案内ページ参照[終了]。
[53]JAIST創立30周年記念 マテリアルズインフォマティクス国際シンポジウム、2月26日(2021)、開催地:オンライン開催(Webex)、詳細は案内ページ参照[終了]。
[54]状態図・計算熱力学研究会 第1回研究会(PDF形式ページ)、6月28日(2021)、開催地:オンライン開催(Webex)、詳細は案内ページ参照[終了]。
[55]第81回スピントロニクス専門研究会:”AI・インフォマティクスを活用したスピントロニクス研究の最前線”、11月19日(2021)、開催地:オンライン開催、詳細は案内ページ参照[終了]。
[56]講演会「マテリアルズインフォマティクスの最先端〜化学産業への展開〜」、9月8日(2023)、開催地:オンライン開催(Zoomウェビナー)、詳細は案内ページ参照[終了]。
[57]マテリアル戦略総合シンポジウム2023、12月5日(2023)、開催地:一橋大学一橋講堂 学術総合センター2F(千代田区、東京)、詳細は案内ページ参照[終了]。
[58]データ活用社会創成シンポジウム2023、12月12日(2023)、開催地:Zoom によるオンライン開催、詳細は案内ページ参照[終了]。
[59]特別企画「化学における情報・AI の活用」、3月21日(2024)、開催地:日本大学理工学部 船橋キャンパス A1423(14号館 [2階] 1423)(船橋、千葉)、詳細は案内ページ参照[終了]。
[60]第176回 フロンティア材料研究所学術講演会『データ駆動型アプローチによる電気化学材料の開発加速』、5月30日(2024)、開催地:東京工業大学すずかけ台キャンパス 大学会館多目的ホール(地図亜法焚I諭⊃斉狎遏法⊂楮戮楼篤皀據璽源仮[終了]。
[入門講座情報(含む基礎講座)]
第47回薄膜・表面物理 基礎講座:データサイエンスを活用した固体材料・表面研究の最前線、11月16日(2018)、開催地:東京理科大学 森戸記念館 第一フォーラム(新宿、東京)、詳細は案内ページ参照[終了]。
第49回薄膜・表面物理 基礎講座:情報データ科学に基づく結晶材料・界面・プロセス工学の新展開 〜実験との連携運用術〜、11月13日(2020)、開催地:オンライン開催、詳細は案内ページ参照[終了]。
[若手の会情報]
第六回ケモインフォマティクス若手の会、10月25日(2017)、開催地:常盤工業会会館(宇部市、山口)、詳細は案内ページ参照[終了]。
[速習コース情報]
統計数理研究所、H28年度統計思考力育成事業機械学習速習コース、2月9日[終了]、3月7日[終了]、3月28日(2017)[終了]、開催地:TKP渋谷カンファレンスセンター、カンファレンスルーム5A(東京)[現在、定員に達し申込受付停止中]、詳細は案内ページ参照。
[夏の学校情報]
[3]Applied mathematics and machine learning perspectives on Big Data Problems in Computational Sciences、9月30日〜10月4日(2019)、開催地;CECAM-DE-SMSM、詳細は案内ページ参照[終了]。
[秋の学校情報]
ケモインフォマティクス秋の学校(Autumn School
of Chemoinformatics in Tokyo)、11月25日、26日(2015)、開催
地:開催地:東大山上会舘大会議室(本郷、東京)、詳細は案内ページ参照
[終了]。
[討論会情報]
[4]日本金属学会東海支部・日本鉄鋼協会東海支部学術討論会(←PDF形式ページ):「インフォマティクスと連携したモノづくりと計測技術」、1月31日(2018)、開催地:名古屋大学東山キャンパス ES 総合館 1F ESホール(名古屋、愛知)、詳細は案内ページ参照[終了]。
[5]第42回ケモインフォマティクス討論会、10月28日、29日(2019)、開催地:東京大学 山上会館 大会議室(本郷、東京)、詳細は案内ページ参照[終了]。
[6]第43回ケモインフォマティクス討論会、12月9日、10日(2020)、開催地:オンライン開催(Zoom)、詳細は案内ページ参照[終了]。
[Workshop情報](含む説明会、ワークショップ、ミーティング、学会)
[0]Workshop情報(ipam, UCLA)
[28]Open Databases Integration for Materials Design、6月11日〜14日(2019)、開催地:CECAM-HQ-EPFL, Lausanne(スイス)、詳細は案内ページ参照[終了]。
[29]Molecular Kinetics: Sampling, Design and Machine Learning、6月19日〜21日(2019)、開催地:CECAM-DE-MMS、詳細は案内ページ参照[終了]。
[30]Accelerating material discovery by smart high-throughput computations、7月3日〜5日(2019)、開催地:University of Liverpool、詳細は案内ページ参照[終了]。
[31]SPring-8データ科学研究会(第7回)/第43回SPring-8先端利用技術ワークショップ兵庫県マテリアルズ・インフォマティクス講演会(第3回)「放射光計測インフォマティクス」、9月4日(2019)、開催地:(研究会)AP品川アネックス1F A+B室、(技術交流会)AP品川アネックスB1F P室(港区、東京)、詳細は案内ページ参照[終了]。
[32]Deep learning in materials science: interpretation, generalization, and the risk of overfitting、9月23日〜25日(2019)、開催地:Kartause Ittingen (スイス)、詳細は案内ページ参照[終了]。
[33]Thinking outside the box - beyond machine learning for quantum chemistry、10月7日〜11日(2019)、開催地:CECAM-DE-MM1P、詳細は案内ページ参照[終了]。
[34]第3回計算物質科学イノベーションキャンプ:『計算科学とデータ科学が紡ぎ出す計算物質科学の新潮流』、10月8日〜10日(2019)、開催地:ほほえみの宿滝の湯(天童市、山形)、詳細は案内ページ参照[終了]。
[35]Progresses and challenges in modeling activated phenomena: from Machine-Learned energy surface sampling to multi-scaling approaches、11月12日〜15日(2019)、開催地:CNRS-LAAS and University of Toulouse Paul Sabatier、詳細破闇ないページ参照[終了]。
[36](Machine) learning how to coarse-grain、9月28日、29日(2020)、開催地:[virtual event]CECAM-DEーSMSM。詳細は案内ページ参照[終了]。
[37]計算科学研究センター・ナノテクノロジープラットフォーム事業合同ワークショップ、1月12日、13日(2021)、開催地:Zoomによるオンライン配信。詳細は案内ページ参照[終了]。
[38]第11回材料系ワークショップ 〜マテリアルDXを加速する「富岳」を活用した成果の創出に向けて〜、2月10日(2021)、開催方法:web会議システムWebexを使用したオンラインワークショップ。詳細は案内ページ参照[終了]。
[39]Innovative strategies for neurodegenerative diseases: a perspective from molecular simulation, machine learning and experiment、3月24日〜3月26日(2021)、開催地:Scuola Normale Superiore, Pisa(イタリア)[Multinodal event: CECAM-IT-SISSA-SNS, CECAM-DE-JUELICH, CECAM-ISR]。詳細は案内ページ参照[終了]。
[40]BiGmax Workshop 2021、4月14日〜4月16日(2021)、開催地:ポツダム(独)。詳細は案内ページ参照[終了]。
[41]Open Databases Integration for Materials Design、6月7日〜6月11日(2021)、開催地:オンライン開催。詳細は案内ページ参照[終了]。
[42]AI for Materials Science: Mining and Learning Interpretable, Explainable, and Generalizable Models from from Data、6月9日〜6月23日(2021)、開催地:オンライン開催。詳細は案内ページ参照[終了]。
[43]Local Structure meets machine learning in soft matter systems、6月28日〜7月1日(2021)、開催地:オンライン開催。詳細は案内ページ参照[終了]。
[44]第12回材料系ワークショップ 〜マテリアルズインフォマティクスにおける「富岳」の活用に向けて〜、10月6日(2021)、開催地:web会議システムWebexを使用したオンラインワークショップ。詳細は案内ページ参照[終了]。
[45]データ創出・活用型磁性材料研究拠点ワークショップ(文部科学省 データ創出・活用型マテリアル研究開発プロジェクトFS)、11月22日(2021)、開催地:現地/Zoomのハイブリッド開催。詳細は案内ページ参照[終了]。
[46]MADICES workshop 2022: "Machine actionable data for chemical sciences: Bridging experiments, simulations, and machine learning for spectral data"、2月7日〜9日(2022)、開催地:オンライン開催。詳細は案内ページ参照[終了]。
[47]Recent Advances in Machine Learning Accelerated Molecular Dynamics、3月16日〜3月18日(2022)、開催地:CECAM-ITーSISSAーSNS。詳細は案内ページ参照[終了]。
[48]Understanding plastic deformation via Artificial Intelligence、3月28日〜3月30日(2022)、開催地:CECAM-Lugano, Lugano(スイス)。詳細は案内ージ参照[終了]。
[49]Machine Learning for Quantum Many-Body Physics、4月4日〜4月8日(2022)、開催地:CECAM-FR-GSO, Le Village by CA Toulouse Evenement(仏)。詳細は案内ページ参照[終了]。
[50]Young Researcher's Workshop on Machine Learning for Materials、5月9日〜5月13日(2022)、開催地:CECAM-IT-SISSA, Miramare Campus, Via Beirut 4, Grignano (TS) (伊)。詳細は案内ページ参照[終了]。
[51]Machine Learning Augmented Sampling for the Molecular Sciences、5月11日〜5月13日(2022)、開催地:CECAM-HQ-EPFL, Lausanne(スイス)。詳細は案内ページ参照[終了]。
[52]Open Databases Integration for Materials Design、5月30日〜6月3日(2022)、開催地:CECAM-HQ-EPFL, Lausanne(スイス)Grand Hotel Vesuvio, SorrentoGrand Hotel Vesuvio, Sorrento。詳細は案内ページ参照[終了]。
[53]Chasing CVs using Machine Learning: from methods development to biophysical applications、6月28日〜6月30日(2022)、開催地:Inria Paris(仏)。詳細は案内ページ参照[終了]。
[54]Charting large materials dataspaces: AI methods and scalability、10月10日〜10月12日(2022)、開催地:CECAM-FR-RA, Grenoble(仏)。詳細は案内ページ参照[終了]。
[55]Machine Learning Meets Statistical Mechanics: Success and Future Challenges in Biosimulations、10月12日〜10月14日(2022)、開催地:CECAM-IT-SIMUL, Grand Hotel Vesuvio, Sorrento(伊)。詳細は案内ページ参照[終了]。
[56]第34回CCSEワークショップ「原子力材料研究開発におけるDX推進の現状と将来:原子力材料研究開発の革新と新展開」、2月24日(2023)、開催地:オンライン開催(zoom)。詳細は案内ページ参照[終了]。
[57]Actively Learning Materials Science (ALMS 2023)、2月27日〜3月3日(2023)、開催地:CECAM-FI。詳細は案内ページ参照[終了]。
[58]Biomolecular simulation and machine learning in the exascale era: first applications and perspectives、6月1日〜6月3日(2023)、開催地:CECAM-IT-SISSA-SNS。詳細は案内ページ参照[終了]。
[59]Open Databases Integration for Materials Design、6月5日〜6月9日(2023)、開催地:CECAM-HQ-EPFL, Lausanne(スイス)。詳細は案内ページ参照[終了]。
[60]Machine-learned potentials in molecular simulation: best practices and tutorials、7月5日〜7月7日(2023)、開催地:CECAM-AT。詳細は案内ページ参照[終了]。
[61]HIerarchical Structure and Machine Learning (HISML) 2023、10月2日〜10月13日(2023)、開催地:東大物性研 本館6階 第5セミナー室(A615)(柏、千葉)。詳細は案内ページ参照[終了]。
階層型方程式と機械学習、10月4日[終了]、10日(2023)[終了]、開催地:東大物性研 本館6階 大講義室(A632)。詳細は案内ページ参照。
[62]ワークショップ:力学の未来(材料強度分野)、10月4日(2023)、開催地:東大生産技術研究所 食堂コマニ+オンライン(ハイブリッド開催)。詳細は案内ページ参照[終了]。
[63]Blending the DFT-based multiple scattering Greens’ function approach to spectroscopies with machine learning、10月30日〜11月10日(2023)、開催地:CECAM-FR-RA, LES HOUCHES SCHOOL OF PHYSICS。詳細は案内ページ参照[終了]。
[64]Machine Learning Interatomic Potentials: Theory and Practice、11月6日〜11月10日(2023)、開催地:CECAM-FI。詳細は案内ページ参照[終了]。
[65]ワークショップ:力学の未来(マテリアル分野:生成AIと知財)、11月8日(2023)、開催地:東大生産技術研究所 中セミナー室1(An401・402)+オンライン(ハイブリッド開催)。詳細は案内ページ参照[終了]。
[66]Quantum2 on machine learning enhanced sampling、11月29日〜12月1日(2023)、開催地:CECAM-HQ-EPFL, Lausanne(スイス) & online (ハイブリッド開催)。詳細は案内ページ参照[終了]。
[67]「R&D懇話会231回」化学系R&DのDX、12月11日(2023)、開催地:オンライン開催。詳細は案内ページ参照[終了]。
[68]Bringing together rare event sampling, excited state dynamics and machine learning、2月26日〜2月29日(2024)、開催地:CECAM-AT, University of Vienna, Faculty of Chemistry(墺)。詳細は案内ページ参照[終了]。
[69]Machine-actionable data interoperability for the chemical sciences (MADICES 2)、4月22日〜4月25日(2024)、開催地:Zuse Institute Berlin (CECAM-DE-MMS)(独)。詳細は案内ページ参照[終了]。
[70]Machine Learning Modalities for Materials science、5月13日〜5月17日(2024)、開催地:Ljubljana(スロベニア)。詳細は案内ページ参照[終了]。
[71]From Machine-Learning Theory to Driven Complex Systems and back、5月22日〜5月24日(2024)、開催地:CECAM-HQ-EPFL, Lausanne(スイス)。詳細は案内ページ参照[終了]。
[72]Open Databases Integration for Materials Design、6月10日〜6月14日(2024)、開催地:CECAM-HQ-EPFL, Lausanne(スイス)。詳細は案内ページ参照[終了]。
[73]Machine Learning of First Principles Observables、7月8日〜7月12日(2024)、開催地:Zuse Institute Berlin(独)。詳細は案内ページ参照[終了]。
[74]L2M3: Large language models for materials, molecules and beyond、7月9日〜7月12日(2024)、開催地:CECAM-HQ-EPFL, Lausanne(スイス)。詳細は案内ページ参照[終了]。
[75]Machine Learning Potentials: From Interfaces to Solution、8月27日〜8月29日(2024)、開催地:Ruhr University Bochum(独)。詳細は案内ページ参照[終了]。
[76]Machine Learning Interatomic Potentials and Accessible Databases、9月9日〜9月11日(2024)、開催地:Grenoble(仏)。詳細は案内ページ参照[終了]。
[77]Leveraging Machine Learning for Sampling Rare Events in Biomolecular Systems、9月17日〜9月19日(2024)、開催地:CECAM-DE-SMSM。詳細は案内ページ参照[終了]。
[78]Expanding the Impact of Molecular Simulations by Integrating Machine Learning with Statistical Mechanics、10月10日〜10月12日(2024)、開催地:Grand Hotel Vesuvio, Sorrento(伊)。詳細は案内ページ参照[終了]。
[79]Density Functional Theory and Artificial Intelligence learning from each other、3月3日〜3月6日(2025)、開催地:CECAM-HQ-EPFL, Lausanne(スイス)。詳細は案内ページ参照。
[国際会議情報]
[3]Physics Informed Machine Learning、1月19日〜22日(2016)、米国で開催[終了]。
[4]MRM2023/IUMRS-ICA2023、Symposium A-5:"Advanced Algorithms and Tools for Materials Informatics"、12月11日〜12月16日(2023)、開催地:Kyoto International Conference Center(京都)、詳細は案内ページ参照[終了]。
[ハードウェア関連情報][小目次]
[1]Tesla M40(NVIDIA) ← 機械学習向け:紹介記事ペー
ジ(PC Watch)
[2]米Googleが公表した"TPU"(深層学習専用プロセッサ):紹介記
事
ページ(ITpro日経BP)
[3]Intel、機械学習に特化した72コアのXeon Phiを投入:紹介記事ページ(PC Watch)
[組織・機関等情報][小目次]
[1]データ科学的観点から推進する新たな物質・材料研究ハブの開設:情報統合型物質・材料開発イニシアティブ[情報統合型物質・材料研究拠点]←詳細は、機構のプレスリリース情報参照。
[2]2015年11月1日付け:機能材料コンピュテーショナルデザイン研究センター発足(プレスリリース)。関連組織:人工知能研究センター(*)(産総研)
関連情報:東北大学片平キャンパスに「産総研・東北大 数理先端材料モデリングオープンイノベーションラボラトリ」(MathAM-OIL)を設立←産総研のプレスリリース記事
[3](情報)統計数理研究所とSAS Institute Japanが共同でビッグデータ分析の研究基盤、ビッグデータイノベーションラボ(BIL)を設立:統計数理研究所のプレスリリース記事。
[4]東京工業大学に「産総研・東工大 実社会ビッグデータ活用オープンイノベーションラボラトリ」(RWBC-OIL)を設立(← - 実社会ビッグデータ活用技術による新たな価値創造を実現 -):産総研のプレスリリース記事。
[5]データ科学がもたらす「ものづくり」革新 〜 統計数理研究所が新センターを設立 〜:統計数理研究所のプレスリリース記事(新センター名は、”ものづくりデータ科学研究センター”)
[6]データ駆動表面科学研究部会(9/12、2023、アドレス変更を確認)(日本表面真空学会)
[7]科学研究費助成事業「新学術領域研究(研究領域提案型)」:次世代物質探索のための離散幾何学
[8]「高性能磁石の開発に役立つ材料データプラットフォームの運用を - 世界最大規模の希土類磁性材料データベースと人工知能を利用した設計で材料開発を加速へ - 」、産総研のプレスリリース記事。
[文献情報][小目次]
[1]雑誌「表面科学」(日本表面科学会)、2015年10月号、特集:マテリアルズ・インフォマティクス - 表面科学のビッグデータの構築 -
[2]寺倉清之、日本物理学会誌:「物質科学における新しい帰納的アプローチ Keyword: マテリアルズ・インフォマティクス」、第73巻、第3号、132頁(2018)
[3]応用物理学会 薄膜・表面物理分科会:ニュースレター「ビッグデータを活用した新材料研究 〜マテリアルズインフォマティクスは、研究者の経験と勘とをいかに支援するのか!〜」、No. 162 (2018年3月)
[4]化学と工業:特集「マテリアルズ・インフォマティクス」、第71巻、8月号、650頁〜667頁(2018)
[5]計算工学:特集「データ同化の活用に向けて」、第24巻、第1号、4頁〜20頁(2019)
[6]野村悠祐、山地洋平、今田正俊、日本物理学会誌:「機械学習を用いて量子多体系を表現する」、第74巻、第2号、72頁(2019)
[7]応用物理学会薄膜・表面物理分科会 NEWS LETTER No. 166 (March 2019):巻頭言「データサイエンスを活用した固体材料・表面研究の最前線」藤田大介、その他、データ科学、機械学習、ニューラルネットワーク等関連論文あり。
[8]表面と真空:特集「データ駆動科学による表面・真空科学研究の新展開」、第62巻、3月号(2019)
[9]吉田亮、岩山めぐみ、グォ チョンリャン、応用物理:「材料研究の逆問題と機械学習」、第90巻、第7号、428頁(2021)
(参考文献、参考サイト、参考情報等)[小目次]
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- Shin Kiyohara, Hiromi Oda, Koji Tsuda and Teruyasu Mizoguchi,
Jpn. J. Appl. Phys. 55 (2016) 045502[Acceleration][Stable
interface structure][Searching][kriging approach]
- (インフォマティクスとそれに関連しそうな情報)
- https://arxiv.org/abs/2405.11404[How big is Big Data?]
- https://arxiv.org/abs/2312.04600[Haldane bundles][Dataset for learning][Predict][Chern number][Line bundle][Torus]
- https://arxiv.org/abs/2401.00035[Learning about structural errors][Models of complex dynamical systems]
- https://arxiv.org/abs/2309.07844[Predicting][Mechanical properties][Spring network]
- https://arxiv.org/abs/2307.05390[CrysMMNet][Multimodal representation][Crystal property prediction]
- https://arxiv.org/abs/2409.04737[CrysAtom][Distributed representation of Atoms][Crystal property prediction]
- https://arxiv.org/abs/2403.11996[Accelerating scientific discovery][Generative knowledge extraction][Graph-based representation][Multimodal intelligent graph reasoning]
- https://arxiv.org/abs/2409.05556[SciAgents][Automating scientific discovery][Multi-agent intelligent graph reasoning]
- https://arxiv.org/abs/2407.06053[Learning local equivariant representation][Quantum operator]
- https://arxiv.org/abs/2409.06498[Learning local and semi-local density functionals][Exact exchange-correlation potentials and energies]
- https://arxiv.org/abs/2311.10205[Case study][Multi-modal][Multi-institutional data management][Combinatorial materials science community]
- https://arxiv.org/abs/2307.05380[Optimized crystallographic graph generation][Material science]
- https://arxiv.org/abs/2305.16486[Disorder-driven localization][Electron interaction][BixTeI thin film]
- https://arxiv.org/abs/2302.14102[Connectivity optimized nested graph network][Crystal structure]
- https://arxiv.org/abs/2406.13265[Molecule graph network][Many-body equivariant interaction]
- https://arxiv.org/abs/2404.06628[Quantum graph model][Transport][Filamentary switching]
- https://arxiv.org/abs/2304.02354[Neural cellular automata][Solidification microstructure modelling]
- https://arxiv.org/abs/2309.16195[Reconstructing microstructure][Statistical descriptor][Neural cellular automata]
- https://arxiv.org/abs/2305.19856[Cellular automata][Inspired][Multistable origami metamaterial][Mechanical learning]
- https://arxiv.org/abs/2203.02160[Reservoir computing][Spin wave][Skyrmion crystal]
- https://arxiv.org/abs/2209.06962[Task-adaptive physical reservoir computing]
- https://arxiv.org/abs/2301.02193[Universal scaling][Wave speed and size][Nanoscale high-performance reservoir computing][Propagating spin-wave]
- https://arxiv.org/abs/2307.09138[Few-molecule reservoir computing][Experimentally demonstrated][Surface enhanced Raman scattering][Ion-gating stimulation]
- https://arxiv.org/abs/2310.06497[Video recognition][Physical reservoir computing][Magnetic material]
- https://arxiv.org/abs/2310.11743[Moire synaptic transistor][Homogeneous-architecture reservoir computing][Chin. Phys. Lett. 40(11), 117201 (2023)]
- https://arxiv.org/abs/2401.15067[Universality condition][Unified classical][Quantum reservoir computing]
- https://arxiv.org/abs/2409.09886[Memory-augmented quantum reservoir computing]
- https://arxiv.org/abs/2404.04023[Phase binarization][Mutually synchronized][Bias field-free spin Hall nano-oscillator][Reservoir computing]
- https://arxiv.org/abs/2406.13825[Low frequency noise][Nanoparticle-molecule network][Implication][In-materio reservoir computing]
- https://arxiv.org/abs/1902.07495[Beyond scaling relation][Description of catalytic materials]
- https://arxiv.org/abs/1802.06900[Exploring][High-pressure][Materials genome]
- https://arxiv.org/abs/1905.00601[Exploring inorganic and nontoxic double perovskites][Material selection][Device design][Material genome approach]
- https://arxiv.org/abs/1809.09202[Map][Inorganic ternary metal nitride]
- https://arxiv.org/abs/2111.10874[Dataset][Solution-based inorganic materials synthesis recipe][Extracted][Scientific literature]
- https://arxiv.org/abs/2206.08917[Open catalyst 2022 (OC22) dataset][Challenge][Oxide electrocatalysis]
- https://arxiv.org/abs/2008.07016[Contact map][Crystal structure prediction][Global optimization]
- https://arxiv.org/abs/1705.08613[Crystal structure prediction][Incomplete experimental data]
- https://arxiv.org/abs/2309.04475[Crystal structure prediction][Joint equivariant diffusion]
- https://arxiv.org/abs/2306.03099[CrystalGPT][Enhancing system-to-system transferability][Crystallization prediction][Control][Time-series-transformer]
- http://arXiv.org/abs/1509.02515[Dimensionless mapping][Combinatorial algorithm][Design invisible dopants]
- https://arxiv.org/abs/2201.11203[AI-aided mapping][Structure-composition-conductivity relationship][Glass-ceramic lithium thiophosphate electrolyte]
- https://arxiv.org/abs/2206.14634[AI powered][Automated discovery][Polymer membrane][Carbon capture]
- https://arxiv.org/abs/2309.03398[Automated discovery][Wurtzite solid solution][Enhanced piezoelectric response]
- https://arxiv.org/abs/2311.13808[Automated model training][(AMT) GUI][Opportunity][Integrating AI][Laboratory experiment]
- https://arxiv.org/abs/2303.09453[Knowledge discovery][Atomic structure][Feature importance]
- http://arxiv.org/abs/1607.07826[High throughput combinatorial method][Fast and robust prediction][Lattice thermal conductivity]
- https://arxiv.org/abs/2112.06649[Hypothesis learning][Automated experiment][Application][Combinatorial materials libraries]
- https://arxiv.org/abs/2310.15124[Mixed-variable global sensitivity analysis][Knowledge discovery][Efficient combinatorial materials design]
- https://arxiv.org/abs/2106.01616[Data management plan][Importance of data management][BIG-MAP project]
- http://arXiv.org/abs/1602.06402[Band structure diagram paths][Crystallography]
- http://arxiv.org/abs/1501.00691[Distribution of phonon lifetime]
- http://arxiv.org/abs/1503.07503[Motif-based fingerprints]
- http://arxiv.org/abs/1503.07327[Global structural prediction]
- https://arxiv.org/abs/2108.13590[Mining insight][Metal-organic framework synthesis][Scientific literature text]
- http://arxiv.org/abs/0808.2125[Data mining]
- https://arxiv.org/abs/1709.03151[Novel organic high-Tc superconductors][Data mining][Density of states similarity search]
- https://arxiv.org/abs/1710.07721[Data mining][Better material synthesis][Pulsed laser deposition][complex oxides]
- https://arxiv.org/abs/2006.14128[Identifying candidate host][Quantum defect][Data mining]
- https://arxiv.org/abs/2103.03199[Data mining][Dashboard][Statistical analysis][Powerful framework][Chemical design][Molecular nanomagnet]
- https://arxiv.org/abs/2109.04929[Data mining][Terahertz generation crystal]
- https://arxiv.org/abs/2111.07892[Data privacy protection][Microscopic image analysis][Material data mining]
- https://arxiv.org/abs/2206.00076[Exploring][Two-dimensional van der Waals heavy-fermion material][Data mining theoretical approach]
- https://arxiv.org/abs/2301.05691[Structural phase transition][Perovskite][BaCeO3][Revisited][Data mining-cum-first-principles theoretical approache]
- https://arxiv.org/abs/2007.02085[Data-mining][Element Charge][Inorganic material]
- https://arxiv.org/abs/2012.14815[Data-mining][Dislocation microstructure][Coarse-graining][Internal energy]
- https://arxiv.org/abs/2206.11355[Data-mining][In-Situ TEM experiment][Towards understanding][Nanoscale fracture]
- https://arxiv.org/abs/2210.00478[Data-mining][In-situ TEM Experiment][Dynamics][Dislocation][CoCrFeMnNi Alloy]
- https://arxiv.org/abs/2306.00363[Database mining][First-principles assessment][Organic proton-transfer][Ferroelectric]
- https://arxiv.org/abs/2405.20217[Data-efficient fine-tuning][Foundational model][First-principles quality sublimation enthalpies]
- https://arxiv.org/abs/2306.07138[Discovering ferroelectric plastic (ionic) crystal][Cambridge structural database][Database mining][Computational assessment]
- https://arxiv.org/abs/2306.11296[ChatGPT chemistry assistant][Text mining][Prediction][MOF synthesis]
- https://arxiv.org/abs/2409.12182[LifeGPT][Topology-agnostic generative pretrained transformer model][Cellular automata]
- https://arxiv.org/abs/2405.19076[Cephalo][Multi-modal vision-language model][Bio-inspired materials analysis and design]
- https://arxiv.org/abs/2406.17295[MatText][Language model][Text & scale][Materials modeling]
- https://arxiv.org/abs/2109.15290[MatSciBERT][Materials domain language model][Text mining][Information extraction]
- https://arxiv.org/abs/2311.06303[MatNexus][Comprehensive text mining][Analysis suite][Materials discover]
- https://arxiv.org/abs/2408.04661[MaterioMiner][Ontology-based text mining dataset][Extraction][Process-structure-property entities]
- https://arxiv.org/abs/2409.14572[Evaluating][Performance][Robustness][LLM][Materials science Q&A][Property prediction]
- https://arxiv.org/abs/2306.06283[14 Examples][How LLMs can transform materials science and chemistry][Reflection][Large language model hackathon]
- https://arxiv.org/abs/2310.07197[MatChat][Large language model][Application service platform][Materials science]
- https://arxiv.org/abs/2310.08511[HoneyBee][Progressive instruction finetuning][Large language model][Materials Science]
- https://arxiv.org/abs/2310.19998[Generative retrieval-augmented ontologic graph][Multi-agent strategies][Interpretive large language model-based materials design]
- https://arxiv.org/abs/2311.08166[MechAgents][Large language model][Multi-agent collaboration][Mechanics problem][Generate new data][Integrate knowledge]
- https://arxiv.org/abs/2311.13778[Accurate prediction][Experimental band gap][Large language model-based data extraction]
- https://arxiv.org/abs/2312.01291[Opportunities][Retrieval][Tool augmented large language model][Scientific facilities]
- https://arxiv.org/abs/2403.03154[Quantum many-body physics calculation][Large language model]
- https://arxiv.org/abs/2403.06949[Materials science][Era][Large language model][Perspective]
- https://arxiv.org/abs/2403.19783[AlloyBERT][Alloy property prediction][Large language model]
- https://arxiv.org/abs/2406.13163[LLMatDesign][Autonomous materials discovery][Large language model]
- https://arxiv.org/abs/2406.15499[Exploring large language model][Microstructure evolution][Materials][Materials Today Communications, 40, 109583 (2024)]
- https://arxiv.org/abs/2407.07016[Large language model][Predict][Synthesizability][Precursors of crystal structures]
- https://arxiv.org/abs/2407.16867[From text to insight][Large language model][Materials science data extraction]
- https://arxiv.org/abs/2409.03444[Fine-tuning large language model][Domain adaptation][Exploration][Training strategies][Scaling][Model merging][Synergistic capabilities]
- https://arxiv.org/abs/2409.06080[Regression][Large language model][Materials and molecular property Prediction]
- https://arxiv.org/abs/2409.06756[Beyond designer's knowledge][Generating materials design hypotheses][Large language model]
- https://arxiv.org/abs/2409.13732[TopoChat][Enhancing topological materials retrieval][Large language model][Multi-source knowledge]
- https://arxiv.org/abs/2305.08264[MatSci-NLP][Evaluating scientific language model][Materials science language task][Text-to-schema modeling]
- https://arxiv.org/abs/2310.00475[Generative design][Inorganic compound][Deep diffusion language model]
- https://arxiv.org/abs/2204.11953[Crystal transformer][Self-learning neural language model][Generative][Tinkering design]
- https://arxiv.org/abs/2206.13578[Materials transformer][Language model][Generative materials design][Benchmark study]
- https://arxiv.org/abs/2209.14803[polyBERT][Chemical language model][Enable fully machine-driven ultrafast polymer informatics]
- https://arxiv.org/abs/2410.12375[PRefLexOR][Preference-based recursive language modeling][Exploratory optimization][Reasoning and agentic thinking]
- https://arxiv.org/abs/2209.08203[ChemNLP][Natural language processing based library][Materials chemistry text data]
- https://arxiv.org/abs/2310.10445[MechGPT][Language-based strategy][Mechanics][Materials modeling][Connect][Knowledge across scale][Discipline][Modalities]
- https://arxiv.org/abs/2310.11458[GPT-4][Interface][Researcher][Computational software][Improving usability][Reproducibility]
- https://arxiv.org/abs/2312.05468[Image and data mining][Reticular chemistry][GPT-4V]
- http://arxiv.org/abs/1109.6935[Possible high-temperature superconductors][Data-filtering algorithms]
- http://arxiv.org/abs/1007.4838[Strong topological insulators]
- http://arxiv.org/abs/1504.01163 ←AiiDA関連
- AiiDA(Automated Interactive Infrastructure and Database for Atomistic simulations)
- https://arxiv.org/abs/2003.12476[AiiDA 1.0][Scalable computational infrastructure][Automated reproducible workflow][Data provenance]
- https://arxiv.org/abs/2007.10312[Workflow][AiiDA][Engineering][High-throughput][Event-based engine][Robust][Modular computational workflow]
- https://arxiv.org/abs/2010.02731[AiiDAlab][Ecosystem][Developing][Executing][Sharing][Scientific workflow]
- https://arxiv.org/abs/2303.12465[AiiDA-defects][Automated][Fully reproducible workflow][Complete characterization][Defect chemistry][Functional material]
- https://arxiv.org/abs/2301.11689[Rule-free workflow][Automated generation][Database][Scientific literature]
- https://arxiv.org/abs/2309.10923[Semi-automatic staging area][High-quality structured data extraction][Scientific literature]
- https://arxiv.org/abs/2302.04397[Designing workflow][Materials characterization]
- https://arxiv.org/abs/2408.12732[Segment anything model][Grain characterization][Hard drive design]
- https://arxiv.org/abs/1706.08704[Seamlessly share computed materials properties][Full provenance][Integration][AiiDA][TCOD]
- https://arxiv.org/abs/2107.09619[Open-science platform][Computational materials science][AiiDA][Materials cloud][Andreoni W., Yip S. (eds.), Handbook of Materials Modeling. Springer, Cham (2018)]
- https://arxiv.org/abs/2304.03949[Capturing dynamical correlations][Implicit neural representation]
- https://arxiv.org/abs/2203.08205[Learning deep implicit Fourier neural operators][IFNOs][Application][Heterogeneous material modeling]
- https://arxiv.org/abs/2408.15852[chemtrain][Learning deep potential model][Automatic differentiation][Statistical physics]
- https://arxiv.org/abs/2207.03239[Learning][Stress-strain field][Digital composite][Fourier neural operator]
- https://arxiv.org/abs/2305.00478[Domain agnostic][Fourier neural operator]
- https://arxiv.org/abs/2403.18597[Heterogeneous peridynamic neural operator][Discover biotissue constitutive law][Microstructure][Digital Image Correlation Measurement]
- https://arxiv.org/abs/2003.08315[AiiDA-KKR plugin][Application to high-throughput impurity embedding][Topological insulator]
- https://arxiv.org/abs/2111.15229[AiiDA-spirit plugin][Automated spin-dynamics simulation][Multi-scale modelling][First-principles calculation]
- https://arxiv.org/abs/2003.12510[Materials Cloud][Platform][Open computational science]
- https://arxiv.org/abs/2210.11301[Novel materials][Materials cloud 2D database]
- HP5 - Materials Informatics(NCCR-MARVEL)
- http://arxiv.org/abs/1504.01219[VdW]
- http://arxiv.org/abs/1505.03994[First-principles interatomic potentials]
- https://arxiv.org/abs/2111.11120[Meta-learning][Interatomic potential model][Accelerated materials simulation]
- https://arxiv.org/abs/2205.06643[Design space][E(3)-equivariant][Atom-centered interatomic potential]
- https://arxiv.org/abs/1801.09897[Open material property library][Native simulation tool integration][MASTO]
- https://arxiv.org/abs/2410.12771[Open Materials 2024][OMat24][Inorganic materials dataset and models]
- https://arxiv.org/abs/2107.03017[Self-learning hybrid Monte Carlo method][Isothermal-isobaric ensemble][Application to liquid silica]
- https://arxiv.org/abs/2305.05060[Genomic materials design][CALculation of PHAse Dynamics]
- https://arxiv.org/abs/1809.04785[Tracking atomic structure evolution][Directed electron beam induced Si-atom motion][Graphene][Deep machine learning]
- https://arxiv.org/abs/2110.14006[Deep machine learning potential][Multicomponent metallic melt][Development][Predictability][Compositional transferability]
- https://arxiv.org/abs/2201.11835[Deep machine learning potential][Atomistic simulation][Fe-Si-O system][Earth's outer core condition]
- https://arxiv.org/abs/2208.04119[Deep machine learning][Reconstructing lattice topology][Strong thermal fluctuation]
- https://arxiv.org/abs/2404.17852[Melting temperature][Iron under the Earth's inner core condition][Deep machine learning]
- https://arxiv.org/abs/2203.00393[Deep potential][Materials science]
- https://arxiv.org/abs/2201.04243[Two wrongs can make a right][Transfer learning approach][Chemical discovery][Chemical accuracy]
- https://arxiv.org/abs/2303.03000[Transfer learning][Large dataset][Accurate prediction][Material properties]
- https://arxiv.org/abs/1909.11234[Exploring diamond-like lattice thermal conductivity crystal][Feature-based transfer learning]
- https://arxiv.org/abs/2107.13841[Addressing materials' microstructure diversity][Transfer learning]
- https://arxiv.org/abs/2308.13096[Electronic structure prediction][Multi-million atom system][Uncertainty quantification][Enabled transfer learning]
- https://arxiv.org/abs/2308.13917[Transfer learning][Microstructure segmentation][CS-UNet][Hybrid algorithm][Transformer][CNN Encoder]
- https://arxiv.org/abs/2310.11423[Predicting polymerization reaction][Transfer learning][Chemical language model]
- https://arxiv.org/abs/2311.06179[Cluster expansion][Transfer learning][Empirical potential]
- https://arxiv.org/abs/2403.07526[Physics-transfer learning][Material strength screening]
- https://arxiv.org/abs/2403.12982[Knowledge-reuse transfer learning method][Molecular and material science]
- https://arxiv.org/abs/2407.20975[Transfer learning][Multi-material classification][Transition metal dichalcogenide][Atomic force microscopy]
- https://arxiv.org/abs/2208.13294[Leveraging low-fidelity data][Improve machine learning][Sparse high-fidelity][Thermal conductivity data][Transfer learning]
- https://arxiv.org/abs/2408.04042[Scaling law][Sim2Real transfer learning][Expanding computational materials database][Real-world prediction]
- https://arxiv.org/abs/2409.15675[Northeast materials database (NEMAD)][Enabling discovery of high transition temperature magnetic compound]
- https://arxiv.org/abs/2408.11322[Transfer learning][Early estimation][Single-photon source quality][Machine learning method]
- https://arxiv.org/abs/2304.13076[Redundancy][Large material dataset][Efficient and robust learning][Less data]
- https://arxiv.org/abs/2312.00038[Physics-constrained NeuralODE approach][Robust learning][Stiff chemical kinetics]
- https://arxiv.org/abs/2304.10382[Conditional generative model][Learning stochastic processes]
- https://arxiv.org/abs/2310.15994[Training model][Force][Stochastic electronic structure method]
- https://arxiv.org/abs/1611.05481[Predicting][Lattice thermal conductivity][Solving the Boltzmann transport equation][AFLOW][AAPL an automated][Accurate and effcient framework]
- https://arxiv.org/abs/2312.14936[PerCNet][Periodic complete representation][Crystal graph]
- https://arxiv.org/abs/1907.00907[Self-learning projective quantum Monte Carlo simulation][Restricted Boltzmann machine]
- https://arxiv.org/abs/1810.02352[Efficient representation][Topologically ordered state][Restricted Boltzmann machine]
- https://arxiv.org/abs/1809.08631[Efficient algorithmic way][Construct Boltzmann machine representation][Arbitrary stabilizer code]
- https://arxiv.org/abs/1812.08171[Restricted Boltzmann machine][Matrix product state][1D translational invariant stabilizer Codes]
- https://arxiv.org/abs/1903.03076[Ground state phase diagram][One-dimensional Bose-Hubbard model][Restricted Boltzmann machine]
- https://arxiv.org/abs/1910.13454[Multi-layer restricted Boltzmann machine representation][1D quantum many-body wave function]
- https://arxiv.org/abs/2003.07280[Restricted Boltzmann machine representation][Groundstate and excited states][Kitaev Honeycomb model]
- https://arxiv.org/abs/2003.09765[Simulating disordered quantum system][Dense and sparse restricted Boltzmann machine]
- https://arxiv.org/abs/2009.14777[Helping restricted Boltzmann machine][Quantum-state representation][Restoring symmetry]
- https://arxiv.org/abs/2402.02794[Convolutional restricted Boltzmann machine (CRBM)][Correlated variational wave function][Hubbard model][Square lattice][Mott metal-insulator transition]
- https://arxiv.org/abs/2103.08804[Chebyshev expansion][Spectral function][Restricted Boltzmann machine]
- https://arxiv.org/abs/2110.03676[Pruning][Restricted Boltzmann machine][Quantum state reconstruction]
- https://arxiv.org/abs/2202.07576[Accuracy][Restricted Boltzmann Machine][One-dimensional J1−J2 Heisenberg model]
- https://arxiv.org/abs/2404.11229[Mean field initialization][Annealed importance sampling algorithm][Efficient evaluation][Partition function][Restricted Boltzmann machine]
- https://arxiv.org/abs/2407.01451[Representing arbitrary ground state][Toric code][Restricted Boltzmann machine]
- https://arxiv.org/abs/2407.11165[Restricted Boltzmann machine][Modeling complex physical system][Case study][Artificial spin ice]
- https://arxiv.org/abs/2409.06146[Configuration interaction guided sampling][Interpretable restricted Boltzmann machine]
- https://arxiv.org/abs/2005.01547[Atomic Boltzmann machine][Capable][On-chip learning]
- https://arxiv.org/abs/2102.05137[Hardware-aware][In-situ Boltzmann machine learning][Stochastic magnetic tunnel junction]
- https://arxiv.org/abs/2103.04791[Purifying deep Boltzmann machine][Thermal quantum state][Phys. Rev. Lett. 127, 060601 (2021)]
- https://arxiv.org/abs/2302.03212[Statistical approach][Topological entanglement][Boltzmann machine representation][Higher-order irreducible correlation]
- https://arxiv.org/abs/2306.16877[Boltzmann machine][Quantum many-body problem]
- http://arxiv.org/abs/1506.00303[AFLOW]
- http://arxiv.org/abs/1607.02532[AFLOW][Library of Crystallographic Prototypes]
- https://arxiv.org/abs/1611.05714[Combining the AFLOW GIBBS][Elastic libraries][Efficiently and robustly screening thermo-mechanical properties]
- https://arxiv.org/abs/1711.10744[AFLOW-ML][RESTful API][Machine-learning prediction]
- https://arxiv.org/abs/1909.02255[Self-learning hybrid Monte Carlo][First-principles Approach]
- https://arxiv.org/abs/1612.05130[AFLUX][LUX materials search API][AFLOW data repositories]
- https://arxiv.org/abs/1701.06921[AFLOWπ][A minimalist approach][High-throughput ab initio calculation][Generation of tight-binding hamiltonians]
- https://arxiv.org/abs/1712.00422[AFLOW][Fleet for Materials Discovery]
- https://arxiv.org/abs/1802.07977[AFLOW-SYM][Platform][Complete][Automatic][Self-consistent][Symmetry analysis of crystals]
- https://arxiv.org/abs/1806.06901[AFLOW-CHULL][Cloud-oriented platform][Autonomous phase stability analysis]
- https://arxiv.org/abs/2111.07478[Physics in the Machine][Integrating physical knowledge][Autonomous phase-mapping][CAMEO][AFLOW]
- https://arxiv.org/abs/1806.07864[AFLOW library][Crystallographic prototypes][Part 2]
- https://arxiv.org/abs/1807.04669[AFLOW-QHA3P][Robust and automated method][Compute thermodynamic properties of solids]
- https://arxiv.org/abs/1902.00485[Metallic glass][Biodegradable implant][AFLOW]
- https://arxiv.org/abs/2007.01988[Reproducibility][High-throughput density functional theory][Comparison][AFLOW][Materials Project][OQMD]
- https://arxiv.org/abs/2010.04222[AFLOW-XtalFinder][Reliable choice][Identify crystalline prototypes]
- https://arxiv.org/abs/2012.05961[AFLOW library][Crystallographic prototype][Part 3]
- https://arxiv.org/abs/2101.02724[Automated coordination][Corrected enthalpy][AFLOW-CCE]
- https://arxiv.org/abs/2207.09842[aflow.org][Web ecosystem][Database][Software][Tool]
- https://arxiv.org/abs/2208.03052[aflow++][C++ framework][Autonomous materials design]
- https://arxiv.org/abs/2310.16769[AFLOW for alloys]
- https://arxiv.org/abs/2401.01852[Machine learned interatomic potential][Ternary carbide][AFLOW Database]
- https://arxiv.org/abs/2401.06875[AFLOW library][Crystallographic prototypes][Part 4]
- https://arxiv.org/abs/1805.05309[Automated computation][Materials property]
- https://arxiv.org/abs/2309.02646[Benchmarking inverse optimization algorithm][Molecular materials discovery]
- https://arxiv.org/abs/2005.00707[Benchmarking materials property prediction method][Matbench test set][Automatminer reference algorithm]
- https://arxiv.org/abs/2405.10448[Dynamic in-context learning][Conversational model][Data extraction][Materials property prediction]
- https://arxiv.org/abs/1710.11611[Online search tool][Graphical patterns][Electronic band structure]
- https://arxiv.org/abs/1808.03383[Pattern learning][Electronic density of states]
- https://arxiv.org/abs/2006.11803[Learning the electronic density of states][Condensed matter]
- https://arxiv.org/abs/1901.09487[2DMatPedia][Open computational database][Two-dimensional materials][Top-down and bottom-up approaches]
- https://arxiv.org/abs/1801.10560[Benchmark database][Transition metal surface][Adsorption energy][Many-body perturbation theory]
- https://arxiv.org/abs/1802.05192[Charge transfer network][Alternative topological understanding][Electronic structure][Proteins Database]
- https://arxiv.org/abs/2111.02081[Molecular bond engineering][Feature learning][Design][Hybrid organic-inorganic perovskites solar cell][Strong non-covalent halogen-cation interaction][SISSO]
- https://arxiv.org/abs/1901.00948[Simultaneous learning][Several materials properties][Incomplete databases][Multi-Task SISSO]
- https://arxiv.org/abs/2305.01242[Recent advances][SISSO method][Implementation][SISSO++ code]
- https://arxiv.org/abs/1902.05241[Database of novel magnetic materials][High-performance][Permanent magnet development]
- https://arxiv.org/abs/2008.10402[Numerical quality control][DFT-based materials database]
- https://arxiv.org/abs/2106.07987[Database construction][Two-dimensional material-substrate interface]
- https://arxiv.org/abs/2311.09891[Some elusive aspects][Databases hindering AI based discovery][Case study][Superconducting material]
- https://arxiv.org/abs/2010.15166[Polymer informatics][Multi-Task learning]
- https://arxiv.org/abs/2403.10042[Accurate][Data-efficient micro-XRD phase identification][Multi-task learning][Application][Hydrothermal fluid]
- https://arxiv.org/abs/2405.12229[Multi-task learning][Molecular electronic structure][Approaching coupled-cluster accuracy]
- https://arxiv.org/abs/2205.13757[Representing polymer][Periodic graph][Learned descriptor][Accurate polymer property prediction]
- https://arxiv.org/abs/2306.05261[Representing][Learning function][Invariant][Crystallographic group]
- https://arxiv.org/abs/2303.14046[Update][DScribe library][New descriptors and derivatives]
- https://arxiv.org/abs/2303.14872[Local concentration-based descriptor predicting][Stacking fault energy][Refractory high entropy alloy]
- https://arxiv.org/abs/1902.10838[Data-centric online ecosystem][Digital materials science]
- http://arXiv.org/abs/1511.04373[Modeling Disordered Materials][High Throughput ab-initio Approach]
- http://arXiv.org/abs/1603.06924[High-Throughput prediction][Finite-temperature properties][Quasi-harmonic approximation]
- http://arxiv.org/abs/1605.05188[Topologically close-packed phases][Binary transition-metal compounds][Matching high-throughput][Empirical structure map]
- http://arxiv.org/abs/1602.01725[High-Throughput ab-initio Dilute Solute Diffusion Database]
- https://arxiv.org/abs/1610.04279[High-throughput first principles search][New ferroelectrics]
- https://arxiv.org/abs/1611.06246[Mapping and classifying molecules][High-throughput structural database]
- https://arxiv.org/abs/1702.02734[First-principles high-throughput screening][Shape-memory alloy][Energetic][Dynamical][Structural]
- https://arxiv.org/abs/1711.03426[Search for high entropy alloys][High-throughput][Ab-initio approach]
- https://arxiv.org/abs/1801.02948[High-throughput][Identification][Electrides][All Known Inorganic Materials]
- https://arxiv.org/abs/1811.11702[Surface structure search][Coarse graining][Statistical learning]
- https://arxiv.org/abs/1805.09199[High-throughput study][Static dielectric constant][High temperatures][Oxide and fluoride cubic perovskites]
- https://arxiv.org/abs/1806.09733[High-throughput screening algorithm][Relativistic Density Functional Theory][Find chelating agent][Separation][Radioactive waste]
- https://arxiv.org/abs/1807.01273[High-throughput investigation][Tunable][Superconductivity][FeSe film]
- https://arxiv.org/abs/1807.05623[Accessible computational materials design][High fidelity][High throughput]
- https://arxiv.org/abs/1807.07821[Looking for new thermoelectric materials][TMX intermetallics][High-throughput calculation]
- https://arxiv.org/abs/1808.02684[High throughput screening][Spin-gapless semiconductor][Quaternary Heusler compound]
- https://arxiv.org/abs/1808.04733[High-throughput descriptor][Predicting potential topological insulators][Tetradymite family]
- https://arxiv.org/abs/1808.05325[Electronic properties of binary compounds][High fidelity][High throughput]
- https://arxiv.org/abs/1809.01132[Designing materials][High refractive index][Wide band gap][High-throughput]
- https://arxiv.org/abs/1809.04751[High-throughput calculation][Thermal conductivity][Nanoporous material][Half-Heusler compound]
- https://arxiv.org/abs/1810.09402[Monopole mining method][High throughput screening][Weyl semimetal]
- https://arxiv.org/abs/1810.11775[ADAIS: Automatic Derivation of Anisotropic Ideal Strength][High-throughput][First-principles]
- https://arxiv.org/abs/1811.05390[Computationally-driven][High throughput identification][CaTe][Li3Sb][Promising candidate][High mobility][p-type][Transparent conducting]
- https://arxiv.org/abs/1812.06222[Inorganic electride][High-throughput screening]
- https://arxiv.org/abs/1901.05121[Discovery of hidden classes][Layered electride][Extensive high-throughput materials screening]
- https://arxiv.org/abs/1901.02261[First-principles study][Structural transition][LiNiO2][High throughput screening][Long life battery]
- https://arxiv.org/abs/1903.01286[Determining the optimal phase-change Material][High-throughput calculation]
- https://arxiv.org/abs/1903.11466[High throughput computational screening][2D ferromagnetic material][Critical role of anisotropy][Local correlation]
- https://arxiv.org/abs/1904.06047[Structure map of AB2 type 2D materials][High-throughput DFT calculation]
- https://arxiv.org/abs/1906.06317[freud][Software suite][High throughput analysis][Particle simulation data]
- https://arxiv.org/abs/2009.06268[High throughput screening][Transition metal binuclear site][N2 fixation]
- https://arxiv.org/abs/1909.10189[High throughput production][Transparent conductive single-walled carbon nanotube film][Advanced floating catalyst chemical vapor deposition]
- https://arxiv.org/abs/2206.09943[High throughput investigation][Emergent][Naturally abundant 2D material][Clinochlore]
- https://arxiv.org/abs/1907.03811[High-throughput extraction][Anisotropic interdiffusion coefficient][hcp Mg-Al alloy]
- https://arxiv.org/abs/1909.00433[Automated][High-throughput][Wannierisation]
- https://arxiv.org/abs/1909.04195[High dielectric ternary Oxide][Crystal structure prediction][High-throughput screening]
- https://arxiv.org/abs/1909.09605[Spin-orbitronic material][Record spin-charge conversion][High-throughput ab initio calculation]
- https://arxiv.org/abs/2011.10905[Discovery][Extreme work function][High-throughput density functional theory][Machine learning]
- https://arxiv.org/abs/1909.10395[Accelerated discovery][Two-dimensional optoelectronic octahedral oxyhalide][High-throughput Ab Initio Calculation][Machine learning]
- https://arxiv.org/abs/1909.13297[High-throughput detection][Manipulation][Single nitrogen-vacancy center's charge][Nanodiamond]
- https://arxiv.org/abs/1910.00548[High-throughput][Data-mining][Predict new rare-earth free permanent magnets]
- https://arxiv.org/abs/1910.02984[Accurate high-throughput screening][I-II-V 8-electron Half-Heusler compound][Renewable-energy application]
- https://arxiv.org/abs/1911.08071[SHRY][Suite for High-throughput generation][Atomic substitution][Implemented by python]
- https://arxiv.org/abs/2101.08814[Efficient implementation][Atom-density representation]
- https://arxiv.org/abs/2007.13012[PyXtal FF][Python library][Automated force field generation]
- https://arxiv.org/abs/1912.09330[High-throughput structural and electrochemical study][Metallic glass formation][Ni-Ti-Al]
- https://arxiv.org/abs/1912.09006[Recent advances][High-throughput superconductivity research]
- https://arxiv.org/abs/1912.10593[Screening promising thermoelectric material][Binary chalcogenide][High-throughput Computation]
- https://arxiv.org/abs/2002.05803[Genuine correlation][Piezoelectric][Two-dimensional material][High-throughput computational study]
- https://arxiv.org/abs/2003.00012[High-throughput calculation][Antiferromagnetic topological material][Magnetic topological quantum chemistry]
- https://arxiv.org/abs/2003.03457[Direct and high-throughput fabrication][Mie-resonant metasurface][Single-pulse laser interference]
- https://arxiv.org/abs/2004.07991[Band gap engineering][Sublattice mixing][High-throughput screening][From first-principles]
- https://arxiv.org/abs/2004.09489[High-throughput screening][Weyl semimetal][S4 symmetry]
- https://arxiv.org/abs/2004.14460[High throughput computational screening][Two-dimensional magnetic material][Experimental database][Three-dimensional compound]
- https://arxiv.org/abs/2005.14092[New high-throughput method][Additive manufacturing][Materials design][Processing optimization]
- http://arXiv.org/abs/1603.05967[High-throughput search][New ternary superalloys]
- https://arxiv.org/abs/2006.01075[High-throughput search][Magnetic and topological order][Transition metal oxide]
- https://arxiv.org/abs/2102.00237[High-throughput search][Magnetic topological material][Spin-orbit spillage][Machine-learning][Experiment]
- https://arxiv.org/abs/2306.03095[High-throughput search][Triplet point defect][Narrow emission line][2D material]
- https://arxiv.org/abs/2407.02785[Identifying direct bandgap silicon structures][High-throughput search][Machine learning method]
- https://arxiv.org/abs/2408.11044[High-throughput search][Photostrictive material][Thermodynamic descriptor]
- https://arxiv.org/abs/2409.04632[High-throughput search][Prediction][Layered 4f-material]
- https://arxiv.org/abs/2006.00183[High-throughput screening][Band gap engineering][Sublattice mixing][Cs2AgBiCl6]
- https://arxiv.org/abs/2007.01205[Database][Wannier tight-binding Hamiltonian][High-throughput density functional theory]
- https://arxiv.org/abs/2007.14250[High-throughput production][Cheap mineral-based 2D electrocatalyst][High-current-density hydrogen evolution]
- https://arxiv.org/abs/2007.14047[Good practice guide][Electrical characterization][Graphene][Non-contact][High-throughput]
- https://arxiv.org/abs/2008.00283[Crystallography companion agent][High-throughput materials discovery]
- https://arxiv.org/abs/2008.06838[High-throughput computational characterization][Two-dimensional][Compositionally complex][Transition-metal chalcogenide alloy]
- https://arxiv.org/abs/2008.09784[High-throughput ensemble characterization][Individual core-shell nanoparticle][Quantitative 3D density][XFEL single-particle imaging]
- https://arxiv.org/abs/2103.02039[Demonstration][Laser powder bed fusion combinatorial sample][High-throughput microstructure][Indentation characterization]
- https://arxiv.org/abs/2103.15415[Direct growth][Hexagonal boron nitride][Photonic chip][High-throughput characterization]
- https://arxiv.org/abs/2204.11223[High-throughput characterization][Transition metal dichalcogenide alloy][Thermodynamic stability][Electronic band alignment]
- https://arxiv.org/abs/2212.04804[Computationally accelerated experimental materials characterization][Drawing inspiration][High-throughput simulation workflow]
- https://arxiv.org/abs/2306.17277[Speeding up high-throughput characterization][Materials libraries][Active learning][Autonomous electrical resistance measurement]
- https://arxiv.org/abs/2311.14497[Advancing high-throughput combinatorial aging studies][Hybrid perovskite thin-film][Precise automated characterization method][Machine learning assisted analysis]
- https://arxiv.org/abs/2008.12907[High-throughput][Design][Magnetic material]
- https://arxiv.org/abs/2009.00735[Open][Strong-scaling Tool][Atom probe crystallography][High-throughput method][Indexing crystal structure and orientation]
- https://arxiv.org/abs/2009.01638[MAELAS][Magneto-elastic property][Computational high-throughput approach]
- https://arxiv.org/abs/2009.08048[High-throughput computational-experimental screening protocol][Discovery][Bimetallic catalyst]
- https://arxiv.org/abs/2010.00660[Thermochemical database][High-throughput][First-principles calculation][Application][Analyzing phase evolution][AM-fabricated IN718]
- https://arxiv.org/abs/2010.03543[High-throughput technique][Measuring][Spin Hall effect]
- https://arxiv.org/abs/2010.07168[Tin pest problem][Test of density functionals][High-throughput calculation]
- https://arxiv.org/abs/2011.05225[High-throughput screening][Thermoelastic][Ultra-high temperature ceramics]
- https://arxiv.org/abs/2012.04790[High-throughput computational search][Two-dimensional binary alloy][Energetic stability][Synthesizability][Three-dimensional counterpart]
- https://arxiv.org/abs/2102.05604[Scalable multicomponent spectral analysis][High-throughput data annotation]
- https://arxiv.org/abs/2011.08134[High throughput search][Efficient thermoelectric][Half-Heusler compound]
- https://arxiv.org/abs/2009.12519[High-throughput design][Peierls][Charge density wave][Q1D organometallic material]
- https://arxiv.org/abs/2102.06180[High-throughput][Rapid experimental alloy development][HT-READ]
- https://arxiv.org/abs/2103.09652[Identification of materials][Strong magneto-structural coupling][Computational high-throughput screening]
- https://arxiv.org/abs/2104.10590[First-principles-based approach][High-throughput screening][Corrosion-resistant high entropy alloy]
- https://arxiv.org/abs/1901.09323[Prediction][Silicate glasses' stiffness][High-throughput MD][Machine learning]
- https://arxiv.org/abs/1910.02336[Database-driven high-throughput calculation][Machine learning model][Materials Design]
- https://arxiv.org/abs/2011.10382[Machine learning][High-throughput][Robust design][P3HT-CNT composite thin film][High electrical conductivity]
- https://arxiv.org/abs/2106.02946[Training][Machine-learning driven Gaussian approximation potential][Si-H Interaction]
- https://arxiv.org/abs/2212.04811[Machine-learning driven synthesis][TiZrNbHfTaC5][High-entropy carbide]
- https://arxiv.org/abs/2406.17676[Understanding phase transition][alpha-quartz][Dynamic compression condition][Machine-learning driven atomistic simulation]
- https://arxiv.org/abs/2102.12785[High-throughput nanoindentation mapping][Cast IN718 nickel-based superalloy][Nb concentration]
- https://arxiv.org/abs/2103.00513[Linear-superelastic][Ti-Nb nanocomposite alloy][Ultralow modulus][High-throughput][Phase field simulation and design]
- https://arxiv.org/abs/1909.03511[Beyond ternary OPV][High-throughput experimentation][Self-driving laboratory][Optimize multi-component system]
- https://arxiv.org/abs/2009.08529[Complex solid solution][Electrocatalyst][Discovery][Prediction][High-throughput experimentation]
- https://arxiv.org/abs/2104.10235[Accelerated discovery][Molten salt corrosion-resistant alloy][High-throughput experimental and modeling method][Coupled to data analytics]
- https://arxiv.org/abs/2104.00864[Constrained non-negative matrix factorization][Enabling real-time insight][In situ][High-throughput experiment]
- https://arxiv.org/abs/2105.05160[Research data infrastructure][High-throughput experimental materials science]
- https://arxiv.org/abs/2406.04537[HTESP][High-throughput electronic structure package][High-throughput ab initio calculation]
- https://arxiv.org/abs/2104.02106[Efficient determination][True magnetic structure][High-throughput ab initio screening][MDMC method]
- https://arxiv.org/abs/2104.04173[High-throughput imaging measurement][Thermoelectric figure of merit]
- https://arxiv.org/abs/2104.11150[Towards high-throughput superconductor discovery][Machine learning]
- https://arxiv.org/abs/2105.01329[Microstructural][Compositional design][Mo-V-Nb-Ti-Zr multi-principal element alloy][High-throughput first-principles study]
- https://arxiv.org/abs/1810.10640[High-throughput discovery][Topological material][Spin-orbit spillage]
- https://arxiv.org/abs/2105.02296[High-throughput discovery][High-temperature conventional superconductor]
- https://arxiv.org/abs/2202.05962[High-throughput discovery][Chemical structure-polarity relationship][Combining][Automation][Machine learning]
- https://arxiv.org/abs/2404.19613[High-throughput discovery][Metal oxides][High thermoelectric performance][Interpretable feature engineering][Small data]
- https://arxiv.org/abs/2102.01880[High-throughput discovery][Novel cubic crystal material][Deep generative neural network]
- https://arxiv.org/abs/2410.08501[High-throughput discovery][Kagome material][Transition metal oxide monolayer]
- https://arxiv.org/abs/2307.10728[Sampling][Whole materials space][Conventional superconducting material]
- https://arxiv.org/abs/2106.03312[High-throughput study][Antisolvent][Stability][Multicomponent metal halide perovskite][Robotics-based synthesis][Machine learning]
- https://arxiv.org/abs/2106.11985[High-throughput investigation][Topological][Nodal][Superconductor][Phys. Rev. Lett. 129, 027001 (2022)]
- https://arxiv.org/abs/2107.03966[DFTTK][DFT tool kit][High-throughput calculation][Thermodynamic][Finite temperature]
- https://arxiv.org/abs/2112.03900[Computational synthesis][2D Material][High-throughput approach][Materials Design]
- https://arxiv.org/abs/2112.09543[Exploring][Cs-Te phase space][High-throughput density-functional theory calculation][beyond the generalized-gradient approximation]
- https://arxiv.org/abs/2201.04213[High-throughput determination][Hubbard U][Hund J][Transition metal oxide][Linear response formalism]
- https://arxiv.org/abs/2201.09059[High-throughput calculation][Combining machine learning][Corrosion properties][Binary Mg alloy]
- https://arxiv.org/abs/2208.09647[Combining machine learning][Many-body calculation][Coverage-dependent adsorption][CO on Rh(111)][Phys. Rev. Lett. 130, 078001 (2023)]
- https://arxiv.org/abs/2202.02021[Mutual modulation][Charge transfer][Unpaired electrons][Catalytic site][Superior intrinsic activity][N2 reduction][High-throughput computation][Machine learning perspective]
- https://arxiv.org/abs/2203.08699[High-throughput computation][Li-based battery material database][Chemistry-processing-property relationship]
- https://arxiv.org/abs/2204.03628[High-throughput computational discovery][40 ultralow thermal conductivity][20 highly anisotropic crystalline materials]
- https://arxiv.org/abs/2204.07534[Ultra-fast spectroscopy][High-throughput][Interactive quantum chemistry]
- https://arxiv.org/abs/2204.08963[Rapid screening][High-throughput ground state prediction]
- https://arxiv.org/abs/2204.14065[Holistic determination][Optoelectronic properties][High-throughput spectroscopy][Surface-guided CsPbBr3 nanowire]
- https://arxiv.org/abs/2205.11085[High-throughput screening][Piezo-photocatalytic material][Hydrogen production]
- https://arxiv.org/abs/2205.13510[Strong-scaling][Open-source tool][High-throughput quantification][Material point cloud data][Composition gradient][Microstructural object reconstruction][Spatial correlation]
- https://arxiv.org/abs/2403.13627[Efficient exploration][High-Tc superconductor][Gradient-based composition design]
- https://arxiv.org/abs/2205.13661[High-throughput nanopore fabrication][Classification][FIB irradiation][Automated pore edge analysis]
- https://arxiv.org/abs/2205.14286[High-throughput computation][Structure prototype analysis][Two-dimensional ferromagnetic material]
- https://arxiv.org/abs/2205.14907[High-throughput study][Anomalous Hall effect]
- https://arxiv.org/abs/2206.00078[Magnetic hourglass fermion][Exhaustive symmetry condition][High-throughput materials prediction]
- https://arxiv.org/abs/2206.01969[Doped graphene quantum dot][UV-vis absorption spectrum][High-throughput TDDFT study]
- https://arxiv.org/abs/2206.07108[Framework][Optimal selection][High-throughput data collection workflow][Autonomous experimentation system]
- https://arxiv.org/abs/2207.00364[High-throughput analysis][Fröhlich-type polaron model]
- https://arxiv.org/abs/2207.03365[High-throughput screening][Half-antiperovskite][Stacked kagome lattice]
- https://arxiv.org/abs/2207.05353[Absorption][Adsorption][High-throughput computation][Impurities][2D material]
- https://arxiv.org/abs/2207.05283[High-throughput screening][Transition metal single-atom catalyst][Nitrogen reduction reaction]
- https://arxiv.org/abs/2207.09569[Indirect band gap semiconductor][Thin-film photovoltaics][High-throughput calculation][Phonon-assisted absorption]
- https://arxiv.org/abs/2207.10134[High-throughput screening][Strong electron-phonon coupling][Ternary metal diboride]
- https://arxiv.org/abs/2208.06097[High-throughput][Condensed-phase hybrid density functional theory][Large-scale finite-gap system][SeA approach]
- https://arxiv.org/abs/2209.02918[High-throughput][Optical absorption spectra][Inorganic semiconductor]
- https://arxiv.org/abs/2209.15423[High-throughput computation][Raman spectra][First principles]
- https://arxiv.org/abs/2210.01367[MyElas][Automatized tool-kit][High-throughput calculation][Post-processing][Visualization][Elasticity][Related properties][Solid]
- https://arxiv.org/abs/2210.03486[Ultrasonic delamination][Adhesion testing][High-throughput][van der Waals heteroepitaxy]
- https://arxiv.org/abs/2210.05654[Influence][Chemistry][Structure][Interfacial segregation][NbMoTaW][High-throughput atomistic simulation]
- https://arxiv.org/abs/2210.17428[EC-MOF/Phase-I][Computationally ready database][Electrically conductive metal-organic framework][High-throughput structural and electronic properties]
- https://arxiv.org/abs/2211.05254[High-throughput DFT-based discovery][Next generation][Two-dimensional (2D) superconductor]
- https://arxiv.org/abs/2211.14688[High-throughput ab initio reaction mechanism exploration][Cloud][Automated multi-reference validation]
- https://arxiv.org/abs/2211.16685[High-throughput][Ab Initio design][Atomic interface][InterMatch]
- https://arxiv.org/abs/2212.06289[High-throughput screening assisted discovery][Stable layered anti-ferromagnetic semiconductor][CdFeP2Se6]
- https://arxiv.org/abs/2212.07845[Exploration][All-3d Heusler alloy][Permanent magnet][Ab initio][High-throughput study]
- https://arxiv.org/abs/2301.06407[High-throughput many-body perturbation theory][Efficient algorithm][Automated workflow]
- https://arxiv.org/abs/2401.05875[GPTArticleExtractor][Automated workflow][Magnetic material database construction]
- https://arxiv.org/abs/2301.08841[mkite][Distributed computing platform][High-throughput][Materials simulation]
- https://arxiv.org/abs/2301.10839[Sub-picosecond carrier dynamics][Automated high-throughput studies][Doping inhomogeneity][Bayesian framework]
- https://arxiv.org/abs/2408.08843[Automated high-throughput organic crystal structure prediction][Population-based sampling]
- https://arxiv.org/abs/2302.04896[High-throughput computational dataset][Halide perovskite alloy]
- https://arxiv.org/abs/2302.09966[High-throughput-based examination][Density functional effect][Intrinsic properties][SnS]
- https://arxiv.org/abs/2303.01594[High-throughput identification][Spin-photon interface][Silicon]
- https://arxiv.org/abs/2303.07542[Asynchronous parallel high-throughput model][Calibration framework][Crystal plasticity finite element constitutive model]
- https://arxiv.org/abs/2303.09226[Magnetic electride][High-throughput material screening][Intriguing properties][Application]
- https://arxiv.org/abs/2304.04149[High-throughput][Alloy and process design][Metal additive manufacturing]
- https://arxiv.org/abs/2304.14367[TribChem][Software][First-principles][High-throughput study][Solid interface][Tribological properties]
- https://arxiv.org/abs/2305.07867[Integrated system built][Small-molecule semiconductor][High-throughput approaches]
- https://arxiv.org/abs/2306.09300[Exploring magnetism][Lead-free halide double perovskite][High-throughput first-principles study]
- https://arxiv.org/abs/2306.11116[Na in Diamond][High spin defect][Revealed][ADAQ high-throughput computational database]
- https://arxiv.org/abs/2306.17092[High-throughput design][All-d-metal Heusler alloy][Magnetocaloric application]
- https://arxiv.org/abs/2306.16508[Surface chemical modification][Adhesion][Metallic interface][High-throughput analysis]
- https://arxiv.org/abs/2307.14250[High-throughput density functional theory screening][Double transition metal MXene precursor]
- https://arxiv.org/abs/2308.01663[High-throughput screening][Weyl semimetal]
- https://arxiv.org/abs/2308.14439[High-throughput screening][Heterogeneous transition metal dual-atom catalyst][Synergistic effect][Nitrate reduction][Ammonia][Adv. Funct. Mater. 33, 2301493 (2023)]
- https://arxiv.org/abs/2308.16174[High-throughput assessment][Microstructural stability][Segregation-engineered nanocrystalline][Al-Ni-Y alloy]
- https://arxiv.org/abs/2309.04822[High-throughput first-principles calculation][Screening][Coherent topologically close-packed precipitate][Hexagonal close-packed metallic system]
- http://arXiv.org/abs/1602.07784[MPInterfaces][Materials project][Python tool][High-throughput Computational Screening][Interfacial system]
- https://arxiv.org/abs/1806.04285[High-throughput computational screening][Two-dimensional semiconductor]
- https://arxiv.org/abs/1909.00623[High-throughput computational screening][Solid-state Li-ion conductor]
- https://arxiv.org/abs/2112.05421[High-throughput computational screening][Bipolar magnetic semiconductor]
- https://arxiv.org/abs/2202.09886[High-throughput computational screening][Nanoporous material][Targeted application]
- https://arxiv.org/abs/2309.08032[Substitutional quantum defect][WS2][Discovered][High-throughput computational screening][Fabricated][Site-selective STM manipulation]
- https://arxiv.org/abs/2405.04239[Orbital magnetization][Two-dimensional material][High-throughput computational screening]
- https://arxiv.org/abs/2309.10274[Prediction][Superconductivity][Metallic boron-carbon compound][From 0 to 100 GPa][High-throughput screening]
- https://arxiv.org/abs/2310.00118[Transforming materials discovery][Artificial photosynthesis][High-throughput screening][Earth-abundant semiconductor]
- https://arxiv.org/abs/2310.00823[Unveiling][Regulatory factor][Phase transition][Zeolitic imidazolate framework][High-Throughout calculation][Data mining]
- https://arxiv.org/abs/2310.02597[TurboGenius][Python suite][High-throughput calculation][Ab initio quantum Monte Carlo method]
- https://arxiv.org/abs/2310.12434[Detailed][High-throughput measurement][Composition dependence][Magnetoresistance][Spin-transfer torque][Composition-gradient film][Application][CoxFe1−x (0 x 1) system]
- https://arxiv.org/abs/2310.16591[Intrinsic piezoelectric anisotropy][Tetragonal ABO3 perovskite][High-throughput study]
- https://arxiv.org/abs/2311.03163[SurfFlow][High-throughput surface energy calculation][Arbitrary crystal]
- https://arxiv.org/abs/2312.04146[High-throughput study][Phase constitution][Thin film system][Mg-Mn-Al-O][Li recovery from slags]
- https://arxiv.org/abs/2311.15430[Application][Batch learning][Boosting high-throughput ab initio success rate][Reducing computational effort][Data-driven processes]
- https://arxiv.org/abs/2312.17715[High-throughput combinatorial approach][Synthesis][Lead-free relaxor ferroelectric system]
- https://arxiv.org/abs/2401.01147[Automated segmentation][Large image dataset][Artificial intelligence][Microstructure characterisation][Damage analysis][High-throughput modelling input]
- https://arxiv.org/abs/2401.13211[High-throughput screening][Boride superconductor]
- https://arxiv.org/abs/2402.04726[Phase stability][Mechanical property trend][MAB phase][High-throughput ab initio calculation]
- https://arxiv.org/abs/2403.05952[New direction][Thermoelectrics][Roadmap][High-throughput materials discovery][Advanced device manufacturing]
- https://arxiv.org/abs/2404.02437[High-throughput calculation][Antiferromagnets hosting anomalous transport phenomena][Phys. Rev. B 109, 094435 (2024)]
- https://arxiv.org/abs/2405.02519[Computationally efficient method][Calculating electron-phonon coupling][High-throughput superconductivity search]
- https://arxiv.org/abs/2406.15630[High-throughput][Data-driven computational framework][Novel quantum material]
- https://arxiv.org/abs/2407.04413[High-throughput magnetic co-doping][Design][Exchange interaction][Topological insulator]
- https://arxiv.org/abs/2407.09228[High-throughput identification][Statistical analysis][Atomically thin semiconductor]
- https://arxiv.org/abs/2408.05594[Emission spectrum][Doped graphene quantum dot][High-throughput TDDFT analysis]
- https://arxiv.org/abs/2408.16851[New magnetic topological material][High-throughput search]
- https://arxiv.org/abs/2409.04418[Charting new regions][Cobalt's chemical space][Maximally large magnetic anisotropy][Computational high-throughput study]
- https://arxiv.org/abs/2205.05053[High throughput generative vector autoregression model][Stochastic synapses]
- https://arxiv.org/abs/2203.11794[Strategic high throughput search][Identifying stable Li based half Heusler alloy][Spintronics application]
- https://arxiv.org/abs/2203.08697[New generalized informatics framework][Development][Large scale virtual battery material database]
- https://arxiv.org/abs/2311.13944[Investigating microstructure-property relationship][Nonwoven][Model-based virtual materials testing]
- https://arxiv.org/abs/2203.06821[SpectroLab][Open source matlab based toolbox][High throughput spectroscopy analysis]
- https://arxiv.org/abs/2201.09977[High throughput inverse design][Bayesian optimization][Functionalities][Spin splitting][Two-dimensional compound]
- https://arxiv.org/abs/2111.00366[High throughput study][Compositionally graded][Homogeneous][Fe-Pt thin film]
- https://arxiv.org/abs/2107.06128[Pathway][High throughput QMC][Alloy][2D]
- https://arxiv.org/abs/2105.05096[Exploring][Correlation][Solvent diffusion][Creep resistance][Mg-Ga HCP Alloy][High throughput liquid-solid diffusion couple][Materials and Design 197, 109243 (2021)]
- https://arxiv.org/abs/2104.09817[High throughput optimization][Hard and tough TiN/Ni nanocomposite coating][Reactive magnetron sputter deposition]
- https://arxiv.org/abs/2208.01692[Cloud platform][Automating][Sharing analysis][Raw simulation data][High throughput polymer molecular dynamics simulation]
- https://arxiv.org/abs/2209.13911[Two-dimensional ferroelectrics][High throughput computational screening]
- https://arxiv.org/abs/2210.00152[pyGWBS][High throughput workflow package][GW-BSE calculation]
- https://arxiv.org/abs/2212.02110[Unveiling][Complex structure-property correlation][Defect][2D material][High throughput dataset]
- https://arxiv.org/abs/2302.07988[High throughput discovery][Lightweight corrosion-resistant compositionally complex alloys]
- https://arxiv.org/abs/2302.09804[High throughput aqueous passivation testing methodology][Compositionally complex alloy][Scanning droplet cell]
- https://arxiv.org/abs/2305.11354[Pyrovskite][Software package][High throughput construction][Analysis][Featurization][Two- and three-dimensional perovskite systems]
- https://arxiv.org/abs/2306.08695[GHP-MOFassemble][Diffusion modeling][High throughput screening][Molecular dynamics][Rational discovery][Novel metal-organic framework][Carbon capture]
- https://arxiv.org/abs/2310.10605[ForceGen][End-to-end de novo protein generation][Nonlinear mechanical unfolding response][Protein language diffusion model]
- https://arxiv.org/abs/2309.06712[High throughput sampling][Phase space][Deep learning potential][delta-AlOOH][Geophysical condition]
- http://arXiv.org/abs/1506.02841[Computational 2D materials database]
- https://arxiv.org/abs/1806.03173[Computational 2D materials database][High-throughput modeling][Discovery][Atomically thin crystal]
- https://arxiv.org/abs/2309.11945[Magnetic order][Computational 2D materials database (C2DB)][High throughput spin spiral calculation]
- https://arxiv.org/abs/2401.05992[Two-dimensional altermagnet][High throughput computational screening][Symmetry requirement][Chiral magnon][Spin-orbit effect]
- https://arxiv.org/abs/2405.15226[Procedural construction][Atomistic polyurethane block copolymer model][High throughput simulation]
- https://arxiv.org/abs/2403.18987[Reconfigurable multiplex setup][High throughput electrical characterisation][Cryogenic temperature]
- https://arxiv.org/abs/2407.08957[High throughput screening][Crystal structure prediction][Carrier mobility calculation][Organic molecular semiconductor][Hole transport layer material][Perovskite solar cell]
- https://arxiv.org/abs/2407.09850[Enhancement][Piezoelectric properties][Narrow cerium doping range][Ba1−xCaxTi1−yZryO3][High throughput experiment]
- https://arxiv.org/abs/2407.19614[Multi-GPU RI-HF energies][Analytic gradient][High throughput ab initio molecular dynamics]
- https://arxiv.org/abs/1811.06807[Ab initio search][Polymer crystal][High thermal conductivity]
- https://arxiv.org/abs/1811.06744[Ab initio thermodynamic property][Certain compound][Nd-Fe-B system]
- https://arxiv.org/abs/1902.10501[Atomistic structure learning]
- https://arxiv.org/abs/2302.00100[Physics-informed][Reduced-order learning][First principles][Simulation][Quantum nanostructure]
- https://arxiv.org/abs/2109.11897[Adaptive clustering-based][Reduced-order modeling framework][Fast and accurate modeling][Localized history-dependent phenomena]
- https://arxiv.org/abs/2206.09002[Cluster generation][Deep energy-based model]
- https://arxiv.org/abs/2302.09184[Rapid design][Top-performing metal-organic framework][Qualitative representation][Building block]
- https://arxiv.org/abs/2312.09991[Extrapolation][Polaron properties][Low phonon frequencies][Bayesian machine learning]
- https://arxiv.org/abs/2407.20848[BMach][Bayesian machine][Optimizing Hubbard U parameter][DFT+U][Machine learning]
- https://arxiv.org/abs/2005.08418[Hardware implementation][Bayesian network building block][Stochastic spintronic device]
- https://arxiv.org/abs/1803.05273[Self-optimized construction][Transition rate matrices][Accelerated atomistic simulation][Bayesian uncertainty quantification]
- https://arxiv.org/abs/1807.06868[Multi-objective Bayesian Materials Discovery][Application][Discovery][Precipitation][Strengthened NiTi][Shape memory alloy][Micromechanical modeling]
- https://arxiv.org/abs/1808.02817[Stochastic unfolding][Nanoconfined DNA][Experiment][Model][Bayesian analysis]
- https://arxiv.org/abs/1809.07365[Bayesian strategy][Uncertainty quantification][Thermodynamic property]
- https://arxiv.org/abs/1810.07616[Bayesian determination][Deep eutectic solvent][Lipid monolayer]
- https://arxiv.org/abs/1812.01205[Bayesian Hamiltonian selection][X-ray photoelectron spectroscopy]
- https://arxiv.org/abs/1905.01802[Bayesian framework][Estimation][Single crystal elastic parameter][Spherical indentation stress-strain measurement]
- https://arxiv.org/abs/1906.03396[Bayesian parametric analytic continuation][Green's function]
- https://arxiv.org/abs/1910.10581[Using Bayesian model selection][Advise neutron reflectometry analysis][Langmuir-Blodgett monolayer]
- https://arxiv.org/abs/1912.07636[Scalable Bayesian Hamiltonian learning]
- https://arxiv.org/abs/2308.11400[Hamiltonian learning][Real-space impurity tomography][Topological moire superconductor]
- https://arxiv.org/abs/2002.04977[Critical temperature prediction][Superconductor][Variational Bayesian neural network approach]
- https://arxiv.org/abs/2311.02495[Uncertainty quantification][Multivariable regression][Material property prediction][Bayesian neural network]
- https://arxiv.org/abs/2406.14838[Bayesian neural network][Predicting uncertainty][Full-field material response]
- https://arxiv.org/abs/1811.00421[Bayesim][Tool for adaptive grid model fitting][Bayesian inference]
- https://arxiv.org/abs/1708.09274[Efficient Bayesian inference][Atomistic structure][Complex functional materials]
- https://arxiv.org/abs/2002.08391[Bayesian inference][Band excitation][Scanning probe microscopy][Optimal dynamic model selection][Imaging]
- https://arxiv.org/abs/2003.02151[Versatile atomic magnetometry assisted][Bayesian inference]
- https://arxiv.org/abs/2008.03595[Bayesian inference framework][Compression][Prediction][Quantum state]
- https://arxiv.org/abs/2012.04765[Bayesian inference][Polycrystalline material]
- https://arxiv.org/abs/2104.05302[Integrating Bayesian inference][Scanning probe experiment][Robust identification][Surface adsorbate configuration]
- https://arxiv.org/abs/2104.08114[AI-driven Bayesian inference][Statistical microstructure descriptor][Finite-frequency wave]
- https://arxiv.org/abs/2402.05799[Recent breakthrough][AI-driven Materials Science][Tech giant][Introduce groundbreaking model]
- https://arxiv.org/abs/2410.10885[Adaptive AI-driven material synthesis][Towards autonomous 2D materials growth]
- https://arxiv.org/abs/2201.04865[Bayesian inference][Microstructural][Hyperelastic model][Tendon deformation]
- https://arxiv.org/abs/2202.12881[Bayesian inference][Fiber orientation][Polymer][Short fiber-reinforced polymer composite]
- https://arxiv.org/abs/2205.09188[Bayesian inference][Hamiltonian selection][Mössbauer spectroscopy][J. Phys. Soc. Jpn. 91, 104002 (2022)]
- https://arxiv.org/abs/2305.05170[Efficient NMR measurement][Data analysis][Bayesian inference][Heavy fermion compound YbCo2Zn20]
- https://arxiv.org/abs/2309.14785[Bayesian inference][Identify crystalline structure][XRD]
- https://arxiv.org/abs/2309.01271[Bayesian inference][Composition-dependent phase diagram]
- https://arxiv.org/abs/2406.08981[Bayesian inference][General noise model parameter][Surface code's syndrome statistics]
- https://arxiv.org/abs/2003.13393[Uncertainty quantification][First principles computational phase diagram][Prediction][Li-Si system][Bayesian sampling]
- https://arxiv.org/abs/2004.09814[Exploring physics][Ferroelectric domain wall][Bayesian analysis][Atomically resolved STEM data]
- https://arxiv.org/abs/2004.11526[Bayesian][Non-parametric Bragg-edge fitting][Neutron transmission strain imaging]
- https://arxiv.org/abs/2401.05848[Pushing][Pareto][Front of band gap][Permittivity][ML-guided search][Dielectric material]
- https://arxiv.org/abs/2005.09325[Benefit][Bayesian analysis][Characterization][Magnetic nanoparticle]
- https://arxiv.org/abs/2006.06125[Bayesian modelling][Pattern formation][One snapshot]
- https://arxiv.org/abs/1904.02042[On-the-fly Bayesian active learning][Interpretable force-field][Atomistic rare event]
- https://arxiv.org/abs/2006.06141[On-the-fly closed-loop autonomous materials discovery][Bayesian active learning]
- https://arxiv.org/abs/2108.08918[On-the-fly autonomous control of neutron diffraction][Physics-informed Bayesian active learning]
- https://arxiv.org/abs/2307.12452[Characterizing non-Markovian quantum processes][Fast Bayesian tomography]
- https://arxiv.org/abs/2008.11796[Fast Bayesian force field][Active learning][Inter-dimensional transformation][Stanene]
- https://arxiv.org/abs/2106.01949[Active learning][Reactive Bayesian force field][Application][Heterogeneous hydrogen-platinum catalysis dynamics]
- https://arxiv.org/abs/2308.07311[Stability][Mechanism][Kinetics][Emergence][Au surface reconstruction][Bayesian force field]
- https://arxiv.org/abs/2401.04359[Unbiased atomistic prediction][Crystal dislocation dynamics][Bayesian force field]
- https://arxiv.org/abs/2010.13241[Bayesian surrogate constitutive model][Estimate][Failure probability][Rubber-like material]
- https://arxiv.org/abs/2011.06086[Bayesian committee approach][Computational physics problem]
- https://arxiv.org/abs/2012.10694[Bayesian unsupervised learning][Hidden structure][Concentrated electrolyte]
- https://arxiv.org/abs/2012.12463[Bayesian learning][Adatom interaction][Atomically-resolved imaging data]
- https://arxiv.org/abs/2102.10976[Highly efficient][Accurate][Ab initio Bayesian active learning method][Accelerating discovery][Advanced functional material]
- https://arxiv.org/abs/2104.09479[Bayesian learning][Thermodynamic integration][Numerical convergence][Accurate phase diagram]
- https://arxiv.org/abs/2103.09777[Robust recognition][Exploratory analysis][Crystal structure][Bayesian deep learning]
- https://arxiv.org/abs/2105.05562[Procedure][3D atomic resolution reconstruction][Atom-counting][Bayesian genetic algorithm]
- https://arxiv.org/abs/2109.07617[Exploring DFT+U parameter space][Bayesian calibration][Markov chain Monte Carlo sampling]
- https://arxiv.org/abs/2110.02046[Beating][Thermal limit][Qubit initialization][Bayesian `Maxwell's demon']
- https://arxiv.org/abs/2112.10851[Bayesian][Frequentist][Information geometry approache][Parametric uncertainty quantification][Classical empirical interatomic potential]
- https://arxiv.org/abs/2202.12218[Robust spin relaxometry][Fast adaptive Bayesian estimation]
- https://arxiv.org/abs/2404.11162[AI-equipped scanning probe microscopy][Autonomous site-specific atomic-level characterization][Room temperature]
- https://arxiv.org/abs/2203.09895[Bayesian spectral deconvolution][X-ray absorption near edge structure][Discriminating][High- and low-energy domain][J. Phys. Soc. Jpn. 91, 074009 (2022)]
- https://arxiv.org/abs/2205.03611[Bayesian estimation][Correlation function]
- https://arxiv.org/abs/2205.15458[Bayesian active learning][Scanning probe microscopy][Gaussian process][Hypothesis learning]
- https://arxiv.org/abs/2206.11343[Bayesian model calibration][Block copolymer self-assembly][Likelihood-free inference][Expected information][Gain computation][Measure transport]
- https://arxiv.org/abs/2207.10406[Advice][Describing Bayesian analysis][Neutron and X-ray reflectometry]
- https://arxiv.org/abs/2208.02374[Bayesian calibration][Interatomic potential][Binary alloy]
- https://arxiv.org/abs/2210.03077[Bayesian autotuning][Hubbard model][Quantum simulator]
- https://arxiv.org/abs/2211.01330[Bayesian nonlocal operator regression (BNOR)][Data-driven learning framework][Nonlocal model][Uncertainty quantification]
- https://arxiv.org/abs/2109.02255[Data-driven learning][3-Point correlation function][Microstructure representation]
- https://arxiv.org/abs/2310.18582[Data-driven learning][Generalized Langevin equation][State-dependent memory][Phys. Rev. Lett. 133, 077301 (2024)]
- https://arxiv.org/abs/2211.03103[Highest melting point material][Searched][Bayesian global optimization][Deep potential molecular dynamics]
- http://arxiv.org/abs/1609.04972[Designing nanostructure][Interfacial phonon transport][Bayesian optimization]
- https://arxiv.org/abs/2003.09036[Computational design][Stable and highly ion-conductive material][Multi-objective Bayesian optimization][Diffusion of oxygen and lithium]
- https://arxiv.org/abs/2004.12512[Guided search][Desired functional response][Bayesian optimization][Generative model][Hysteresis loop shape engineering][Ferroelectrics]
- https://arxiv.org/abs/2006.15006[Efficient cysteine conformer search][Bayesian optimization]
- https://arxiv.org/abs/2007.08757[Ply-drop design][Non-conventional composite][Bayesian optimization]
- https://arxiv.org/abs/2208.13771[Fast Bayesian optimization][Needle-in-a-Haystack problem][Zooming memory-based initialization]
- https://arxiv.org/abs/1903.09385[Bayesian optimization][Chemical composition][Comprehensive framework][RFe12-type magnet compound]
- https://arxiv.org/abs/1907.02577[Data-centric mixed-variable Bayesian optimization][Materials Design]
- https://arxiv.org/abs/1910.01688[Bayesian optimization][Materials Design][Mixed quantitative and qualitative variables]
- https://arxiv.org/abs/2001.09312[Prediction of perovskite-related structure][ACuO3−x (A = Ca, Sr, Ba, Sc, Y, La)][DFT][Bayesian optimization]
- https://arxiv.org/abs/2002.05598[Detecting stable adsorbate][(1S)-camphor on Cu(111)][Bayesian optimization]
- https://arxiv.org/abs/2110.07900[Bayesian optimization package][PHYSBO]
- https://arxiv.org/abs/2110.12642[Exploration][New reconstructed structure][GaN(0001) surface][Bayesian optimization]
- https://arxiv.org/abs/2202.04242[Finding][Stable structure][2D hexagonal material][Bayesian optimization][Beyond the structural relationship][3D crystal][Weakly-bonded binary system]
- https://arxiv.org/abs/2203.07892[Autonomous atomic Hamiltonian construction][Active sampling][x-ray absorption spectroscopy][Adversarial Bayesian optimization]
- https://arxiv.org/abs/2407.04457[Kalman filter][Adversarial Bayesian optimization][Active sampling][Inelastic neutron scattering]
- https://arxiv.org/abs/2011.10968[Crystal structure prediction][Combining graph network][Bayesian optimization]
- https://arxiv.org/abs/2106.01309[Benchmarking][Performance of Bayesian optimization][Multiple experimental materials science domain]
- https://arxiv.org/abs/2106.08212[Bayesian optimization][High-entropy alloy composition][Electrocatalytic oxygen reduction]
- https://arxiv.org/abs/2107.02685[Bayesian optimised collection strategy][Fatigue testing][Constant life testing]
- https://arxiv.org/abs/2107.14005[Commun Phys 4, 170 (2021)][Unveiling quasiparticle dynamics][Topological insulator][Bayesian modelling]
- https://arxiv.org/abs/2108.00002[Bayesian optimization][Materials Science][Survey]
- https://arxiv.org/abs/2108.12889[Multi-objective Bayesian optimization][Ferroelectric material][Interfacial control][Memory and energy storage]
- https://arxiv.org/abs/2203.12597[High-dimensional Bayesian optimization][Hyperparameter][Attention-based network][Predict Materials Property][Case study][CrabNet using Ax and SAASBO]
- https://arxiv.org/abs/2203.17241[Bayesian optimization][Known experimental and design constraints][Chemistry application]
- https://arxiv.org/abs/2204.05452[Bayesian optimization][Experimental failure][High-throughput materials growth]
- https://arxiv.org/abs/2204.11815[Automatic parameter selection][Electron ptychography][Bayesian optimization]
- https://arxiv.org/abs/2206.12435[Bayesian optimization][Continuous space][Virtual process embedding]
- https://arxiv.org/abs/2208.11919[Pareto front analysis][Multi-objective Bayesian optimization][(R, Z)(Fe,Co,Ti)12 (R = Y, Nd, Sm; Z = Zr, Dy)][J. Phys. Soc. Jpn. 92, 014801 (2023)]
- https://arxiv.org/abs/2210.02242[Bayesian optimization][Discrete dislocation plasticity][Two-dimensional precipitation hardened crystal]
- https://arxiv.org/abs/2302.00710[Exploring energy-composition relationship][Bayesian optimization][Accelerated discovery][Inorganic material]
- https://arxiv.org/abs/2303.00929[Stoichiometric growth][SrTiO3 film][Bayesian optimization][Adaptive prior mean]
- https://arxiv.org/abs/2303.08426[Structural disorder][Octahedral tilting][Inorganic halide perovskite][New insight][Bayesian optimization]
- https://arxiv.org/abs/2303.13754[Bayesian optimization][Metastable nickel formation][Spontaneous crystallization][Extreme condition]
- https://arxiv.org/abs/2309.04168[Bayesian optimization][Active learning][Ta-Nb-Hf-Zr-Ti system][Spin transport properties]
- https://arxiv.org/abs/2309.12464[Bayesian optimisation approach][Quantify][Input parameter uncertainty][Prediction][Numerical physics simulation]
- https://arxiv.org/abs/2310.17765[Autonomous convergence][STM control parameter][Bayesian optimization]
- https://arxiv.org/abs/2402.02198[Multimodal Co-orchestration][Exploring structure-property relationship][Combinatorial libraries][Multi-task Bayesian optimization]
- https://arxiv.org/abs/2402.11101[Physics-based material parameters extraction][Perovskite experiment][Bayesian optimization]
- https://arxiv.org/abs/2311.09591[Accelerating material discovery][Threshold-driven hybrid acquisition][Policy-based Bayesian optimization]
- https://arxiv.org/abs/2404.18234[Bayesian optimization][State engineering][Quantum gases]
- https://arxiv.org/abs/2405.03092[Bayesian optimization][Stable properties amid processing fluctuations][Sputter deposition][J. Vac. Sci. Technol. A 42, 033408 (2024)]
- https://arxiv.org/abs/2405.08900[Interoperable multi objective batch Bayesian optimization framework][High throughput materials discovery]
- https://arxiv.org/abs/2405.16230[Active oversight][Quality control][Standard Bayesian optimization][Autonomous experiment]
- https://arxiv.org/abs/2406.14627[Bayesian optimization priors][Efficient variational quantum algorithm]
- https://arxiv.org/abs/2407.07963[Efficient quantum computation][Molecular ground state energies][Bayesian optimization][Priors over surface topology]
- https://arxiv.org/abs/2408.15590[Bayesian optimization][Atomic structure][Prior probabilities][Universal interatomic potential]
- https://arxiv.org/abs/2409.07190[Applying multi-fidelity Bayesian optimization][Chemistry][Open challenge][Major consideration]
- https://arxiv.org/abs/2409.09192[Automated design][Nonreciprocal thermal emitter][Bayesian optimization]
- https://arxiv.org/abs/2410.02717[Measurement][Noise][Bayesian optimization][Co-optimizing noise][Property discovery][Automated experiment]
- https://arxiv.org/abs/2410.04314[Hierarchical Gaussian process-based Bayesian optimization][Materials discovery][High entropy alloy space]
- https://arxiv.org/abs/2304.03573[Bayesian][Seebeck coefficient][Liquid water][Equilibrium molecular dynamics]
- https://arxiv.org/abs/2305.04841[AutoEIS][Automated Bayesian model selection][Analysis][Electrochemical impedance spectroscopy]
- https://arxiv.org/abs/2305.08574[Bayesian learning design][Characterizes porous metamaterial][Biofilm transport and control]
- https://arxiv.org/abs/2306.03575[Quantifying physical insight][Cooperatively][Exhaustive search][Bayesian spectroscopy][X-ray photoelectron spectra]
- https://arxiv.org/abs/2306.05398[Bayesian model calibration][Diblock copolymer][Film self-assembly][Power spectrum][Microscopy data]
- https://arxiv.org/abs/2306.10406[Human-in-the-loop][Bayesian autonomous materials phase mapping]
- https://arxiv.org/abs/2308.07669[Bayesian modelling approaches][Quantum state][Ultimate Gaussian process state][Handbook]
- https://arxiv.org/abs/2309.10812[Bond breaking kinetics][Mechanically controlled break junction experiment][Bayesian approach]
- https://arxiv.org/abs/2406.05133[Hierarchical Bayesian approach][Adaptive integration][Bragg peak][Time-of-flight neutron scattering data]
- https://arxiv.org/abs/2408.03110[Charge state estimation][Quantum dot][Bayesian approach]
- https://arxiv.org/abs/2309.14302[Bayesian parameter estimation][Characterising mobile ion vacancies][Perovskite solar cell]
- https://arxiv.org/abs/2309.15014[Efficient adaptive Bayesian estimation][Slowly fluctuating Overhauser field gradient]
- https://arxiv.org/abs/2311.00763[Quantifying intuition][Bayesian approach][Figures of merit][EXAFS analysis][Magic size cluster]
- https://arxiv.org/abs/2311.06228[Learning material synthesis-structure-property relationship][Data fusion][Bayesian][Co-regionalization][N-Dimensional piecewise function learning]
- https://arxiv.org/abs/2312.16078[Targeted materials discovery][Bayesian algorithm execution]
- https://arxiv.org/abs/2401.00018[Combining Bayesian reconstruction entropy][Maximum entropy method][Analytic continuation][Matrix-valued Green's function]
- https://arxiv.org/abs/2401.06106[Accelerated development][Multicomponent alloy][Discrete design space][Bayesian multi-objective optimisation]
- https://arxiv.org/abs/2401.10466[Quantitative selection][Sample structures][Small-angle scattering][Bayesian method]
- https://arxiv.org/abs/2402.04873[Seebeck coefficient][Ionic conductor][Bayesian regression analysis]
- https://arxiv.org/abs/2402.06256[Bayesian committee machine potential][Isothermal-isobaric molecular dynamics simulation]
- https://arxiv.org/abs/2402.14497[Sparse Bayesian committee machine potential][Hydrocarbon]
- https://arxiv.org/abs/2402.17132[Bayesian committee machine potential][Organic nitrogen compound]
- https://arxiv.org/abs/2403.01158[Bayesian committee machine potential][Oxygen-containing organic compound]
- https://arxiv.org/abs/2403.04645[Accelerating][Convergence][Coupled cluster calculation][Homogeneous electron gas][Bayesian ridge regression]
- https://arxiv.org/abs/2403.09677[Rapid and robust construction][ML-ready peak feature table][X-ray diffraction data][Bayesian peak-top fitting]
- https://arxiv.org/abs/2403.18391[Bayesian electron density determination][Sparse and noisy single-molecule X-ray scattering images]
- https://arxiv.org/abs/2404.12899[Bayesian Co-navigation][Dynamic designing][Materials digital twins][Active learning]
- https://arxiv.org/abs/2404.13092[Scale-bridging][Complex model hierarchy][Investigation][Metal-fueled circular energy economy][Bayesian model calibration][Model error quantification]
- https://arxiv.org/abs/2406.15223[Thermodynamic modeling][LiCl-KCl-LaCl3 system][Bayesian model selection][Uncertainty quantification]
- https://arxiv.org/abs/2409.15307[Adaptive Gaussian process method][Multi-modal Bayesian inverse problem]
- https://arxiv.org/abs/2408.09889[Field][Bayesian evidence inference][Nested sampling data]
- https://arxiv.org/abs/2409.10381[J-UBIK][JAX-accelerated universal Bayesian imaging kit]
- https://arxiv.org/abs/2405.01243[Multimodal reconstruction][TbCo thin film structure][Basyeian analysis][Polarised neutron reflectivity]
- https://arxiv.org/abs/2201.03156[m*][Two-dimensional electron gas][Neural canonical transformation study]
- https://arxiv.org/abs/2402.11103[Toward Learning][Latent-variable representation][Microstructure][Optimizing][Spatial statistics space]
- https://arxiv.org/abs/1907.08473[Static local field correction][Warm dense electron gas][Ab initio path integral Monte Carlo study][Machine learning representation]
- https://arxiv.org/abs/2403.19039[Expanding density-correlation machine learning representation][Anisotropic coarse-grained particle]
- http://arXiv.org/abs/1512.09110[Machine learning method][Interatomic potential][Application to boron carbide]
- http://arxiv.org/abs/1512.03502[Prediction][Grain boundary structure and energy][Machine learning]
- http://arxiv.org/abs/1509.00973[Prediction model of band-gap][AX binary compounds][Combination of density functional theory calculations and machine learning technique]
- http://arXiv.org/abs/1506.08858[Machine learning][Dynamical mean-field theory]
- http://arXiv.org/abs/1508.05315[Machine Learning Energies][2 M Elpasolite (ABC2D6) Crystals]
- https://arxiv.org/abs/1812.02306[Efficient construction method][Phase diagram][Uncertainty sampling]
- http://arXiv.org/abs/1605.01735[Machine learning phases of matter]
- http://arxiv.org/abs/1607.06789[Machine learning][Identify factors][Govern amorphization][Irradiated pyrochlore]
- http://arxiv.org/abs/1608.07374[Machine learning][Atomic forces in a Crystalline Solid][Transferability to Various Temperatures]
- http://arxiv.org/abs/1609.02552[Machine learning phases][Strongly correlated Fermions]
- http://arxiv.org/abs/1609.03705[Pure density functional][Strong correlations][Thermodynamic limit][Machine learning]
- https://arxiv.org/abs/1611.08645[Representation of crystalline compounds][Machine-learning prediction]
- https://arxiv.org/abs/1704.06439[Unified Representation][Machine learning][Molecule][Crystal]
- https://arxiv.org/abs/1705.00565[Machine learning meets quantum state preparation][Phase diagram][Quantum control]
- https://arxiv.org/abs/1808.10869[Phase diagram][All inorganic materials]
- https://arxiv.org/abs/1705.08798[Robust FCC solute diffusion predictions][Ab-initio machine learning]
- https://arxiv.org/abs/1706.00192[Materials screening][Discovery of new half-Heuslers][Machine learning versus Ab initio methods]
- https://arxiv.org/abs/1706.00179[Machine learning][Unifies the modelling of materials and molecules]
- https://arxiv.org/abs/1706.02012[What can a machine learn from a single first-principles calculation?]
- https://arxiv.org/abs/1706.06293[Efficient and accurate machine-learning][Interpolation of atomic energies][Composition][Many Species]
- https://arxiv.org/abs/1810.12534[Computational method][Creation materials][Required composition][Structure]
- https://arxiv.org/abs/2309.06930[Modeling dislocation dynamics data][Semantic web technologies]
- https://arxiv.org/abs/2309.14450[Learning dislocation dynamics][Mobility law][Large-scale MD simulation]
- https://arxiv.org/abs/1904.05690[Materials discovery][Stable][Nontoxic][Halide perovskite][High-efficiency solar cell]
- https://arxiv.org/abs/1902.06573[Expanding the horizon][Automated metamaterials discovery][Quantum annealing]
- https://arxiv.org/abs/2407.08270[SciQu][Accelerating materials properties prediction][Automated literature mining][Self-driving laboratories]
- https://arxiv.org/abs/2309.13673[Alloy informatics][Ab initio charge density profile][Case study][Hydrogen effect][Face-centered cubic crystal]
- https://arxiv.org/abs/1806.09662[Materials informatics approach][Identification][One-band correlated material][Analogous to the cuprates]
- https://arxiv.org/abs/1901.04133[Materials discovery][Properties prediction][Thermal transport][Materials informatics][Mini-review]
- https://arxiv.org/abs/2109.04007[MaterialsAtlas.org][Materials informatics web app platform][Materials discovery][Survey][State-of-the-art]
- https://arxiv.org/abs/1908.00746[Materials informatics][Evolutionary algorithms][Application to search for superconducting hydrogen compound]
- https://arxiv.org/abs/2001.02876[Synergy of binary substitutions][Improving the cycle performance][LiNiO2][Revealed by ab initio materials informatics]
- https://arxiv.org/abs/2004.00925[Revisiting Tc-structure trend][Cuprate superconductor][Materials informatics]
- https://arxiv.org/abs/2205.00829[Function decomposition tree][Causality-first perspective][Systematic description][Materials informatics]
- https://arxiv.org/abs/2210.07683[AI-accelerated materials informatics method][Discovery][Ductile alloy]
- https://arxiv.org/abs/2311.04418[AI-accelerated discovery][Altermagnetic material]
- https://arxiv.org/abs/2409.08065[AI-accelerated discovery][High critical temperature superconductor]
- https://arxiv.org/abs/2310.13136[Approaches for uncertainty quantification][AI-predicted material properties][Comparison]
- https://arxiv.org/abs/2406.16224[From text to test][AI-generated control software][Materials science instrument]
- https://arxiv.org/abs/2210.07929[Object storage][Persistent memory][Data infrastructure][HPC materials informatics]
- https://arxiv.org/abs/1806.06040[Materials informatics][Dark matter detection]
- https://arxiv.org/abs/2305.03797[Materials Informatics][Algorithmic design rule]
- https://arxiv.org/abs/2308.16259[Materials informatics transformer][Language model][Interpretable materials properties prediction]
- https://arxiv.org/abs/2407.04648[Efficient materials informatics][Rockets and Electrons]
- https://arxiv.org/abs/2311.05133[Materials properties prediction][MAPP][Empowering][Solely][Chemical formulas]
- https://arxiv.org/abs/1706.09122[Accurate force field][Molybdenum][Machine learning][Large materials data]
- https://arxiv.org/abs/1707.04826[Machine learning application][Life time of materials]
- https://arxiv.org/abs/1707.07294[Machine learning][Materials informatics][Recent applications and prospects]
- https://arxiv.org/abs/1707.09699[Predicting phase behavior][Grain boundary][Evolutionary search][Machine learning]
- https://arxiv.org/abs/1708.02741[Conceptual and practical bases][High accuracy][Machine learning][Interatomic potential]
- https://arxiv.org/abs/1708.04766[Fundamental band gap][Alignment of two-dimensional semiconductors][Explored by machine learning]
- https://arxiv.org/abs/1708.06017[Resolving transition metal chemical space][Feature selection][Machine learning][Structure-property relationship]
- https://arxiv.org/abs/1708.08530[Machine learning][band gap][Kesterite][Quaternary compound][Photovoltaics application]
- https://arxiv.org/abs/1709.01666[Descriptors][Machine learning][Materials data]
- https://arxiv.org/abs/1709.04576[Catalyst design][Actively learned machine][Non-ab initio input features][CO2 reduction reaction]
- https://arxiv.org/abs/1709.05417[Leaving the valley][Charting the energy landscape][Metal/organic interface][Machine learning]
- https://arxiv.org/abs/1709.06757[Symmetry-adapted machine-learning][Tensorial properties][Atomistic system]
- https://arxiv.org/abs/1710.02605[Optimal mean radius][Volume fraction][Nanocrsytalline phase][Softmagnetic alloy][Machine learning][Calphad approach]
- https://arxiv.org/abs/1710.05677[Linearized machine-learning][Interatomic potential][Non-magnetic elemental metals][Limitation of pairwise descriptors][Trend of predictive power]
- https://arxiv.org/abs/1710.09861[Machine learning][Crystal identification and discovery]
- https://arxiv.org/abs/1712.06018[Predicting][Dissolution kinetics][Silicate glasses][Machine learning]
- https://arxiv.org/abs/1801.04900[Applying machine learning technique][Predict][Energetic material]
- https://arxiv.org/abs/2406.09620[Applying machine learning][Elucidate ultrafast demagnetization dynamics][Ni and Ni80Fe20]
- https://arxiv.org/abs/1802.05377[Novel superhard tungsten nitride][Predicted][Machine-learning accelerated crystal structure searching]
- https://arxiv.org/abs/1803.01416[Machine learning][Determination of atomic dynamics][Grain boundary]
- https://arxiv.org/abs/1803.01035[Machine learning][Entanglement Freedom][How I learned to stop worrying][Love linear regression]
- https://arxiv.org/abs/2102.11868[Machine learning regression][Operator dynamics]
- https://arxiv.org/abs/2008.03670[Lattice thermal conductivity prediction][Symbolic regression][Machine learning]
- https://arxiv.org/abs/2212.04450[GAUCHE][Library][Gaussian processes][Chemistry]
- https://arxiv.org/abs/2006.13426[Non-parametric local pseudopotential][Machine Learning][Tin pseudopotential built][Gaussian process regression]
- https://arxiv.org/abs/2106.08612[Efficient Gaussian process regression][Prediction][Molecular crystal][Harmonic free energy]
- https://arxiv.org/abs/2206.08744[Improved uncertainty quantification][Gaussian process regression][Interatomic potential]
- https://arxiv.org/abs/2307.01407[Enhancing ab initio diffusion calculation][Materials through Gaussian process regression]
- https://arxiv.org/abs/2311.16012[Acceleration][Solvation free energy calculation][Thermodynamic integration][Gaussian process regression][Improved Gelman-Rubin convergence diagnostics]
- https://arxiv.org/abs/2407.12525[Efficient ensemble uncertainty estimation][Gaussian processes regression]
- https://arxiv.org/abs/2312.11487[Symbolic learning][Material discovery]
- https://arxiv.org/abs/1901.04136[Symbolic regression][Materials science]
- https://arxiv.org/abs/2305.19551[Combining first-principles modeling][Symbolic regression][Designing efficient single-atom catalyst][Oxygen evolution reaction][Mo2CO2 MXene]
- https://arxiv.org/abs/2403.10320[Universal crack tip correction algorithm][Physical deep symbolic regression]
- https://arxiv.org/abs/2002.05076[Structure-property map][Kernel principal covariates regression]
- https://arxiv.org/abs/2105.13303[Calibrated bootstrap][Uncertainty quantification][Regression model]
- https://arxiv.org/abs/2107.00638[Symbolic regression][Work function][Metal/organic interfaces][When are descriptors physically meaningful?]
- https://arxiv.org/abs/2206.02040[MetaNOR][Meta-learnt nonlocal operator regression approach][Metamaterial modeling]
- https://arxiv.org/abs/2206.06422[Symbolic regression][Materials Science][Discovering interatomic potential][Data]
- https://arxiv.org/abs/2401.00744[Harmonizing covariance][Expressiveness][Deep Hamiltonian regression][Crystalline material research][Hybrid cascaded regression framework]
- https://arxiv.org/abs/1912.11381[Solving quantum statistical mechanics][Variational autoregressive network][Quantum Circuit]
- https://arxiv.org/abs/2009.05580[Probabilistic formulation][Open quantum many-body systems dynamics][Autoregressive model]
- https://arxiv.org/abs/2210.05871[Autoregressive neural Slater-Jastrow ansatz][Variational Monte Carlo simulation]
- https://arxiv.org/abs/2301.04277[Simulations of Disordered Matter][3D][Morphological autoregressive protocol (MAP)][Water]
- https://arxiv.org/abs/2306.05917[Impact][Conditional modelling][Universal autoregressive quantum state]
- https://arxiv.org/abs/2307.04340[Crystal structure generation][Autoregressive large language modeling]
- https://arxiv.org/abs/2310.04166[Autoregressive neural quantum state][Quantum number symmetries]
- https://arxiv.org/list/cond-mat.mtrl-sci/recent[Intelligent identification][Two-dimensional Structure][Machine-Learning][Optical microscopy]
- https://arxiv.org/abs/1803.06042[Stability engineering][Halide perovskite][Machine learning]
- https://arxiv.org/abs/1803.07059[Autonomous scanning probe microscopy][in-situ][Tip conditioning][Machine Learning]
- https://arxiv.org/abs/1803.09827[Vibrational propertie][Metastable polymorph structure][Machine learning]
- https://arxiv.org/abs/1803.11246[Accelerating materials development][Automation][Machine learning][High-performance computing]
- https://arxiv.org/abs/1804.04651[Machine learning][Computational screening][Inorganic solid electrolytes][Dendrite suppression][Li Metal Anode]
- https://arxiv.org/abs/1804.05924[Applied machine learning][Predict stress hotspot][Hexagonal close packed materials]
- https://arxiv.org/abs/1804.08401[Hybrid piezoelectric-magnetic neurons][Energy-efficient][Machine learning]
- https://arxiv.org/abs/1805.01568[Machine learning][General purpose interatomic potential]
- https://arxiv.org/abs/1805.02303[Machine-learning guided discovery][High-performance][Spin-driven][Thermoelectric material]
- https://arxiv.org/abs/2409.18441[Machine-learning guided search][Phonon-mediated superconductivity][Boron and carbon compounds]
- https://arxiv.org/abs/1805.07325[Machine learning][Force-field inspired descriptor][Fast screening and mapping][Energy landscape]
- https://arxiv.org/abs/1805.10855[Kinetic energy density][Fourth order gradient expansion][Performance][Different classes][Improvement][Machine learning]
- https://arxiv.org/abs/1805.11541[Chemical shift][Molecular solid][Machine learning]
- https://arxiv.org/abs/1806.00829[Machine learning][Quantum phase transition]
- https://arxiv.org/abs/1806.03553[Quantitative trend][8 physical properties of 115000 inorganic compounds][Machine learning]
- https://arxiv.org/abs/1806.03841[Analytic continuation][domain-knowledge free][Machine learning]
- https://arxiv.org/abs/1807.06156[Machine learning][Energetic Material Properties]
- https://arxiv.org/abs/1807.09985[Artificial intelligent][Atomic Force Microscope][Machine learning]
- https://arxiv.org/abs/2005.14422[Single-atom alloy catalyst][Designed][Artificial intelligence]
- https://arxiv.org/abs/2012.01979[Artificial intelligence accelerator][Graphene optoelectronic device]
- https://arxiv.org/abs/2102.08269[Materials gene][Heterogeneous catalysis][Clean experiment][Artificial intelligence]
- https://arxiv.org/abs/2108.02837[Lossless multi-scale constitutive elastic relation][Artificial intelligence]
- https://arxiv.org/abs/2201.05655[Emissivity prediction][Functionalized surface][Artificial intelligence]
- https://arxiv.org/abs/2202.01916[Artificial intelligence][Powered material search engine]
- https://arxiv.org/abs/2204.12968[Accelerating materials-space exploration][Mapping materials properties][Artificial intelligence][Lattice thermal conductivity]
- https://arxiv.org/abs/2208.07612[Accelerating nanomaterials discovery][Artificial intelligence][HPC center]
- https://arxiv.org/abs/2209.09636[Artificial intelligence][Concrete material][Scientometric view]
- https://arxiv.org/abs/2209.11618[Artificial intelligence][Advanced material]
- https://arxiv.org/abs/2211.08179[Artificial intelligence approache][Materials-by-design][Energetic material][State-of-the-art][Challenge][Future direction]
- https://arxiv.org/abs/2303.08162[Artificial intelligence][Artificial material][Moire atom]
- https://arxiv.org/abs/2304.13927[NIMS-OS][Automation software][Implement][Closed loop][Artificial intelligence][Robotic experiment][Materials science]
- https://arxiv.org/abs/2312.02796[Materials expert-artificial intelligence][Materials discovery]
- https://arxiv.org/abs/2401.04070[Accelerating computational materials discovery][Artificial intelligence][Cloud high-performance computing][Large-scale screening][Experimental validation]
- https://arxiv.org/abs/2402.07000[Artificial intelligence-enabled optimization][Battery-grade lithium carbonate production]
- https://arxiv.org/abs/2406.19397[Scanning probe microscopy][Artificial intelligence][Quantum computing]
- https://arxiv.org/abs/2407.10022[AtomAgents][Alloy design and discovery][Physics-aware multi-modal multi-agent artificial intelligence]
- https://arxiv.org/abs/2407.17669[Atomic resolution observation][Nanoparticle surface dynamics][Instabilities][Artificial intelligence]
- https://arxiv.org/abs/2409.10304[Impactful research][Artificial intelligence][Chemistry and materials science]
- https://arxiv.org/abs/2410.03746[Resolution enhancement][Scanning electron micrograph][Artificial intelligence]
- https://arxiv.org/abs/2303.12702[Automatic identification][Crystal Structure][Interface][Artificial-intelligence-based electron microscopy]
- https://arxiv.org/abs/2305.02259[Thermally-driven multilevel non-volatile memory][Monolayer MoS2][Neuro-inspired artificial learning]
- https://arxiv.org/abs/2205.02554[Mapping superconductivity][High-pressure hydride][Superhydra project]
- https://arxiv.org/abs/1807.10751[Committee machine][Vote][Similarity between materials]
- https://arxiv.org/abs/1808.00479[Machine learning][Scientific discovery][Electronic quantum matter visualization experiment]
- https://arxiv.org/abs/1808.01714[Machine learning][valence force field model]
- https://arxiv.org/abs/1808.04519[Machine-learning solver][Modified diffusion equations]
- https://arxiv.org/abs/1808.05292[Structural characterization][Machine learning][Grain boundary energy and mobility]
- https://arxiv.org/abs/1809.03960[Atomic positions independent descriptor][Machine learning][Material properties]
- https://arxiv.org/abs/1809.09203[General machine-learning][Surrogate model][Materials prediction]
- https://arxiv.org/abs/1809.09865[Phase diagram][Disordered higher order topological Insulator][Machine learning]
- https://arxiv.org/abs/1810.02323[Machine learning][Electron correlation][Disordered medium]
- https://arxiv.org/abs/1810.02858[Temperature effect][Phonon dispersion stability][Zirconium][Machine learning-driven][Atomistic simulation]
- https://arxiv.org/abs/2311.06010[Machine learning-driven structure prediction][Iron hydride]
- https://arxiv.org/abs/2406.05142[Machine learning-driven optimization][TPMS][Architected material][Simulated annealing]
- https://arxiv.org/abs/2407.04877[Leveraging data mining][Active learning][Domain adaptation][Multi-stage][Machine learning-driven approach][Efficient discovery][Advanced acidic oxygen evolution electrocatalyst]
- https://arxiv.org/abs/1810.03972[Machine learning clustering technique][Powder X-ray diffraction pattern][Distinguish alloy substitution]
- https://arxiv.org/abs/2005.11660[Feature space][XRD pattern][Constructed][Auto-encoder]
- https://arxiv.org/abs/1810.05586[Machine learning force][Trained by Gaussian process][Liquid state][Transferability][Temperature and pressure]
- https://arxiv.org/abs/1810.05538[Learning multiple order parameters][Interpretable machine]
- https://arxiv.org/abs/1903.02175[Materials development][Interpretable machine learning]
- https://arxiv.org/abs/2006.13552[Predicting][Thermally activated β event][Metallic glass][Interpretable machine learning]
- https://arxiv.org/abs/2010.00532[Persistent homology advance][Interpretable machine learning][Nanoporous material]
- https://arxiv.org/abs/2103.03633[Unveiling][Glass Veil][Elucidating][Optical][Glass][Interpretable machine learning]
- https://arxiv.org/abs/2108.05933[Phase classification][Multi-principal element alloy][Interpretable machine learning]
- https://arxiv.org/abs/2112.00239[Interpretable machine learning][Materials Design]
- https://arxiv.org/abs/2307.07609[Interpretable machine learning][Understand][Performance][Semi local density functionals][Materials thermochemistry]
- https://arxiv.org/abs/2409.14905[Interpretable machine learning][High-strength high-entropy alloy design]
- https://arxiv.org/abs/2410.05658[Identifying highly deformable van der Waals layered chalcogenides][Superior thermoelectric performance][Deformability factor][Interpretable machine learning]
- https://arxiv.org/abs/1810.10170[Machine learning][Band gap and alignment][Nitride semiconductor]
- https://arxiv.org/abs/1810.10042[Efficiently measuring][Quantum device ][Machine learning]
- https://arxiv.org/abs/1810.12814[Band gap prediction][Large organic crystal structure][Machine learning]
- https://arxiv.org/abs/1811.00628[Independent vector analysis][Data fusion][Molecular property prediction][Machine learning]
- https://arxiv.org/abs/1811.01914[Machine learning][Molecular dynamics][Strongly correlated electrons]
- https://arxiv.org/abs/1811.08464[Machine learning][Phase stability][Disorder][Automatic flow framework][Materials discovery][PhD thesis]
- https://arxiv.org/abs/1811.09267[Efficient extraction][High-order force constant][Machine learning][hiphive package]
- https://arxiv.org/abs/1812.02708[Ternary mixed-anion semiconductor][Tunable band gap][Machine-learning][Crystal structure prediction]
- https://arxiv.org/abs/1812.04932[Catching the essence][Hohenberg-Kohn's first theorem][Recreating PES][cluster][Machine learning]
- https://arxiv.org/abs/2004.14442[Practical approach][Hohenberg-Kohn map][Many-body correlation][Learning the electronic density]
- https://arxiv.org/abs/2201.11991[Universal machine learning model][Elemental grain boundary energies]
- https://arxiv.org/abs/2402.09251[Universal machine learning][Kohn-Sham Hamiltonian][Material]
- https://arxiv.org/abs/2403.04217[Performance assessment][Universal machine learning interatomic potential][Challenges and Directions][Materials' surfaces]
- https://arxiv.org/abs/2405.07105[Overcoming systematic softening][Universal machine learning interatomic potential][Fine-tuning]
- https://arxiv.org/abs/2410.13820[Enhancing universal machine learning potential][Polarizable long-range interaction]
- https://arxiv.org/abs/1801.06219[Predicting colloidal crystal][Shape][Inverse design][Machine learning]
- https://arxiv.org/abs/1812.11212[Machine learning][Polymer cloud-point engineering][Inverse design]
- https://arxiv.org/abs/1901.01638[Accurate and transferable machine-learning][Interatomic potential][Silicon]
- https://arxiv.org/abs/2107.02599[Transferable machine-learning scheme][Pure metal][Alloy][Predicting adsorption energy]
- https://arxiv.org/abs/2205.02879[Exploiting ligand additivity][Transferable machine learning][Multireference character across][Transition metal complex ligand]
- https://arxiv.org/abs/2106.10768[Representation][Strategy][Transferable machine learning model][Chemical discovery]
- https://arxiv.org/abs/2404.07181[BAMBOO][Predictive and transferable machine learning force field framework][Liquid electrolyte development]
- https://arxiv.org/abs/2008.03023[Tackling the challenge][Huge materials science search space][Quantum-inspired annealing]
- https://arxiv.org/abs/1901.01963[Machine learning topological phases][Real space]
- https://arxiv.org/abs/1901.01501[Accelerated continuous time quantum Monte Carlo method][Machine learning]
- https://arxiv.org/abs/1901.02717[Reliable][Explainable][Machine learning][Accelerated material discovery]
- https://arxiv.org/abs/1901.05801[Machine learning][Novel thermal-materials Discovery][Early Successes][Opportunities][Challenges]
- https://arxiv.org/abs/1901.08615[Approximating power of machine-learning ansatz][Quantum many-body state]
- https://arxiv.org/abs/1901.09071[Layer dependence][Graphene-diamene phase transition][Epitaxial and exfoliated few-layer graphene][Machine learning]
- https://arxiv.org/abs/1901.10971[Machine-learning][Atomic-scale properties][Physical principles]
- https://arxiv.org/abs/1902.02941[Machine learning][Improved estimator][Magnetization curve][Spin gap]
- https://arxiv.org/abs/1902.03682[Paradigm shift][Electron-based crystallography][Machine learning]
- https://arxiv.org/abs/1902.03486[Machine learning Forcefield][Silicate glass]
- https://arxiv.org/abs/1902.04079[Multi-faceted machine learning][Competing order][Disordered interacting system]
- https://arxiv.org/abs/1902.05158[Atomistic machine learning][Prediction][Understanding]
- https://arxiv.org/abs/2305.05536[Coarse-grained versus fully atomistic machine learning][Zeolitic imidazolate framework]
- https://arxiv.org/abs/2306.15638[Robustness][Local prediction][Atomistic machine learning model]
- https://arxiv.org/abs/2404.12367[Information theory][Unifies atomistic machine learning][Uncertainty quantification][Materials thermodynamics]
- https://arxiv.org/abs/2405.02461[Uncertainty quantification][Propagation][Atomistic machine learning]
- https://arxiv.org/abs/1902.08818[Autonomous atomic scale Manufacturing][Machine learning]
- https://arxiv.org/abs/1902.09776[Predicting Young's modulus][Glass][Sparse Dataset][Machine learning]
- https://arxiv.org/abs/1903.00552[Single-component order parameter][URu2Si2][Uncovered][Resonant ultrasound spectroscopy][Machine learning]
- https://arxiv.org/abs/1903.06651[Accelerated discovery][Efficient solar-cell material][Quantum and machine-learning Methods]
- https://arxiv.org/abs/1903.06813[Machine learning][Voltage of electrode Material][Metal-ion battery]
- https://arxiv.org/abs/1903.08060[Hidden self-energy][Origin of cuprate superconductivity][Revealed by machine learning]
- https://arxiv.org/abs/1903.08482[Investigating ultrafast quantum magnetism][Machine learning]
- https://arxiv.org/abs/1903.10216[Road to accuracy][Machine-learning-accelerated silicon][Ab initio simulation]
- https://arxiv.org/abs/1810.00346[Extracting many-particle entanglement entropy][Observable][Supervised machine learning]
- https://arxiv.org/abs/1901.00788[Classification][Local chemical environment][X-ray absorption spectra][Supervised machine learning]
- https://arxiv.org/abs/2005.08131[Evaluation of synthetic][Experimental training data][Supervised machine learning][Charge state detection][Quantum dot]
- https://arxiv.org/abs/1903.10742[Generative tensor network classification model][Supervised machine learning]
- https://arxiv.org/abs/2206.08841[Random projection][Kernelised leave one cluster][Out cross-validation][Universal baseline][Evaluation tool][Supervised machine learning][Materials properties]
- https://arxiv.org/abs/2211.08591[Exploring supervised machine learning][Multi-phase identification][Quantification][Powder X-ray diffraction spectra]
- https://arxiv.org/abs/2109.00570[Process parameter optimization][Friction stir welding][6061AA][Supervised machine learning regression-based algorithm]
- https://arxiv.org/abs/2309.04067[Prediction][Cu oxidation state][EELS and XAS Spectra][Supervised machine learning]
- https://arxiv.org/abs/1904.00031[NetKet][Machine learning toolkit][Many-body quantum system]
- https://arxiv.org/abs/1904.01486[Unveiling phase transition][Machine learning]
- https://arxiv.org/abs/1904.01756[Atomic-level characterisation][Quantum computer array][Machine learning]
- https://arxiv.org/abs/1904.03780[Linking plastic heterogeneity][Bulk metallic glass][Quench-in structural defect][Machine learning]
- https://arxiv.org/abs/1810.12700[Machine learning][Density functional theory][Hubbard model]
- https://arxiv.org/abs/1811.12425[Classifying snapshots][Doped Hubbard model][Machine learning]
- https://arxiv.org/abs/1901.07900[Characterization][Photoexcited state][Half-filled one-dimensional extended Hubbard model][Machine learning]
- https://arxiv.org/abs/1904.06032[Machine learning][Phase diagram][Half-filled one-dimensional extended Hubbard model]
- https://arxiv.org/abs/2211.09584[Mitigating][Hubbard sign problem][Novel application][Machine learning]
- https://arxiv.org/abs/2302.09507[Predicting structure-dependent Hubbard U parameter][Assessing hybrid functional-level exchange][Machine learning]
- https://arxiv.org/abs/2406.02457[Machine learning Hubbard parameter][Equivariant neural network]
- https://arxiv.org/abs/1904.08875[DScribe][Library of descriptors][Machine learning][Materials Science]
- https://arxiv.org/abs/1904.10423[Data ecosystem][Support machine learning][Materials Science]
- https://arxiv.org/abs/1905.01330[TensorNetwork][Library for physics and machine learning]
- https://arxiv.org/abs/1912.04055[FLAME][Library of atomistic modeling environments]
- https://arxiv.org/abs/1905.02350[Error minimization][Predicting accurate adsorption energy][Machine learning]
- https://arxiv.org/abs/1905.03938[Machine learning-guided synthesis][Advanced inorganic material]
- https://arxiv.org/abs/2404.13022[Machine learning-guided accelerated discovery][Structure-property correlation][Lean magnesium alloy][Biomedical application]
- https://arxiv.org/abs/1905.04312[Machine learning][Quantum state][NISQ era]
- https://arxiv.org/abs/1905.06780[Functional form][Superconducting critical temperature][Machine learning]
- https://arxiv.org/abs/1905.09497[Machine-learning][Interatomic potential][Phonon transport][Perfect crystalline Si][Crystalline Si with vacancies]
- https://arxiv.org/abs/1906.03860[Quantum supremacy][Analog quantum processor][Material science][Machine learning]
- https://arxiv.org/abs/1906.05886[Predicting superhard material][Machine learning][Informed evolutionary structure search]
- https://arxiv.org/abs/1906.06329[TensorNetwork][Machine learning]
- https://arxiv.org/abs/1906.08534[Predicting][Curie temperature][Ferromagnet][Machine learning]
- https://arxiv.org/abs/1906.08888[Performance and cost assessment][Machine learning][Interatomic potential]
- https://arxiv.org/abs/1906.10155[Machine learning][Phase transition][Quantum processor]
- https://arxiv.org/abs/1907.01480[Exploring effective charge][Electromigration][Machine learning]
- https://arxiv.org/abs/1907.10587[Understanding magnetic properties][Actinide-based compound][Machine learning]
- https://arxiv.org/abs/1907.10290[Quantum compressed sensing][Unsupervised tensor network][Machine learning]
- https://arxiv.org/abs/1907.10929[Automatic microscopic image analysis][Moving window local Fourier transform][Machine learning]
- https://arxiv.org/abs/1907.12002[Machine learning][DFT potential energy surface][Inorganic halide perovskite][CsPbBr3]
- https://arxiv.org/abs/1706.01840[Designing magnetism][Fe-based Heusler alloy][Machine learning approach]
- https://arxiv.org/abs/1901.00722[Machine learning approach][Thermal conductivity modeling][Irradiated uranium-molybdenum nuclear fuel]
- https://arxiv.org/abs/2306.08017[Transferable machine learning approach][Predicting electronic structure][Charged defect]
- https://arxiv.org/abs/1901.01972[Machine learning approach][Automated fine-tuning][Semiconductor spin qubit]
- https://arxiv.org/abs/1902.00594[Machine learning approach][Determine the space group of a structure][Atomic pair distribution function][PDF]
- https://arxiv.org/abs/1903.02757[Prediction of atomization energy][Au13+ Cluster][Machine learning approach]
- https://arxiv.org/abs/1908.04604[Valley notch filter][Graphene strain superlattice][Green's function][Machine learning approach]
- https://arxiv.org/abs/2205.15151[Berezinskii-Kosterlitz-Thouless transition][Unsupervised machine learning approach]
- https://arxiv.org/abs/2004.00008[Efficient machine learning approach][Optimizing][Timing resolution][High purity germanium detector]
- https://arxiv.org/abs/2008.08341[Phase formation][Mechanical property][Complex concentrated alloy][Machine learning approach]
- https://arxiv.org/abs/2010.04742[Machine learning approach][Muon spectroscopy analysis]
- https://arxiv.org/abs/2012.08657[Iterative machine learning approach][Discovering unexpected thermal conductivity enhancement][Aperiodic superlattice]
- https://arxiv.org/abs/2107.06044[Parametrization][ Non-bonded force field term][Metal-organic framework][Machine learning approach]
- https://arxiv.org/abs/2107.13149[Machine learning approach][Predict L-edge x-ray absorption spectra][Light transition metal ion compound]
- https://arxiv.org/abs/2110.12887[Descriptor][Intrinsic hydrodynamic thermal transport][Screening][Phonon database][Machine learning approach]
- https://arxiv.org/abs/2112.11537[Accelerating][Theoretical study][Li-polysulphide adsorption][Single-atom catalyst][Machine learning approache]
- https://arxiv.org/abs/2202.00564[Machine learning approach][Quantum phase][Frustrated one dimensional spin-1/2 system]
- https://arxiv.org/abs/2407.14148[Predicting grain boundary segregation][Magnesium alloy][Atomistically informed machine learning approach]
- https://arxiv.org/abs/2211.16576[Physics-based machine learning approach][Modeling][Temperature-dependent yield strength][Superalloy]
- https://arxiv.org/abs/2207.09444[Machine learning approach][Genome][Two-dimensional material][Flat electronic band]
- https://arxiv.org/abs/2208.04172[Impact of physicochemical features][Carbon electrode][Capacitive performance][Supercapacitor][Machine learning approach]
- https://arxiv.org/abs/2307.05211[Data-driven machine learning approach][Predicting yield strength][Additively manufactured multi-principal element alloy]
- https://arxiv.org/abs/2208.12705[Machine learning approach][Predict][Structural and magnetic properties][Heusler alloy families]
- https://arxiv.org/abs/2209.08595[Low-cost machine learning approach][Prediction][Transition metal phosphor][Excited state properties]
- https://arxiv.org/abs/2211.04373[Machine learning approach][Drawing phase diagram][Topological lasing mode]
- https://arxiv.org/abs/2309.07424[Composition][Structure][GGA bandgap prediction][Machine learning approach]
- https://arxiv.org/abs/2407.07736[Prediction][Frequency-dependent optical spectrum][Solid material][Multi-output][Multi-fidelity machine learning approach]
- https://arxiv.org/abs/2406.00042[Rise and fall of Anderson localization][Lattice vibrations][Time-dependent machine learning approach]
- https://arxiv.org/abs/2403.02553[Enhancing magnetocaloric material discovery][Machine learning approach][Autogenerated Database][Large language model]
- https://arxiv.org/abs/2406.07345[Machine learning approach][Classical density functional theory]
- https://arxiv.org/abs/2405.03274[MACE][Machine learning approach][Chemistry emulation]
- https://arxiv.org/abs/2409.01523[Machine learning approach][Vibronically renormalized electronic band structure]
- https://arxiv.org/abs/1908.04762[Prediction magnetocrystalline anisotripy][Fe-Rh thin film][Machine leaning]
- https://arxiv.org/abs/1908.05829[Machine learning][Magnetic parameter][Spin configuration]
- https://arxiv.org/abs/1908.06198[Machine learning][Physical non-local exchange-correlation functional][DFT]
- https://arxiv.org/abs/2410.07972[Learning][Equivariant non-local electron density functional]
- https://arxiv.org/abs/1908.09102[Accelerating small-angle scattering experiment][Simulation-based machine learning]
- https://arxiv.org/abs/1908.10953[Predicting outcome][Catalytic reaction][Machine learning]
- https://arxiv.org/abs/1909.05915[Uncovering atomistic mechanism][Crystallization][Machine learning]
- https://arxiv.org/abs/1909.07080[Machine learning][Force field][Metallic nanoparticle]
- https://arxiv.org/abs/1909.10768[Absorption spectra][Charge transfer][PEDOT nanoaggregate][Machine learning]
- https://arxiv.org/abs/1910.02496[Machine learning design][Trapped-ion quantum spin simulator]
- https://arxiv.org/abs/1910.04603[Machine learning driven synthesis][Few-layered WTe2]
- https://arxiv.org/abs/1910.06291[The Materials Simulation Toolkit][Machine learning][MAST-ML][Automated open source toolkit][Accelerate data-driven materials research]
- https://arxiv.org/abs/1910.10254[Machine learning inter-atomic potential][Generation driven][Active learning][Amorphous][Liquid hafnium dioxide]
- https://arxiv.org/abs/1910.10161[Detection of topological materials][Machine learning]
- https://arxiv.org/abs/1910.11300[Machine learning effective model][Quantum system]
- https://arxiv.org/abs/1910.13453[Vulnerability][Machine learning phases of matter]
- https://arxiv.org/abs/1911.02416[Predicting densities and elastic moduli of SiO2-based glasses][Machine learning]
- https://arxiv.org/abs/1911.02263[Surfing multiple conformation-property landscape][Machine learning][Designing magnetic anisotropy]
- https://arxiv.org/abs/1911.05569[Forecasting system][Computational time][DFT][Multiverse ansatz][Machine Learning][Cheminformatics]
- https://arxiv.org/abs/1911.07571[Casimir effect][Machine learning]
- https://arxiv.org/abs/1911.09578[Engineering quantum current state][Machine learning]
- https://arxiv.org/abs/1911.12576[Optimization][Heterogeneous ternary Li3PO4-Li3BO3-Li2SO4 mixture][Li-ion conductivity][Machine learning]
- https://arxiv.org/abs/1912.03578[Cooling rate][Structural Relaxation][Amorphous drug][Elastically collective nonlinear Langevin equation theory][Machine learning]
- https://arxiv.org/abs/2001.00030[Quantum adversarial][Machine learning]
- https://arxiv.org/abs/2408.09297[Out-of-distribution materials property prediction][Adversarial learning][Fine-tuning]
- https://arxiv.org/abs/1912.13460[Machine learning][Effective Hamiltonian][High entropy alloy]
- https://arxiv.org/abs/2001.02589[Machine learning][Completely automatic tuning][Quantum device faster than human experts]
- https://arxiv.org/abs/2001.04605[Machine learning][Materials modeling][Fundamental][Opportunity][2D material]
- https://arxiv.org/abs/2001.05150[Machine learning analysis][Tunnel magnetoresistance][Magnetic tunnel junction][Disordered MgAl2O4]
- https://arxiv.org/abs/2004.00080[Machine learning analysis][Perovskite oxides grown][Molecular beam epitaxy]
- https://arxiv.org/abs/2112.09601[Joint machine learning analysis][Muon spectroscopy data][Different materials]
- https://arxiv.org/abs/2306.11853[Generalization across experimental parameter][Machine learning analysis][High resolution transmission electron microscopy dataset]
- https://arxiv.org/abs/2409.17321[Machine learning analysis][Structural data][Predict electronic properties][Near-surface InAs quantum well]
- https://arxiv.org/abs/2001.06728[Big-data science][Porous material][Materials genomics][Machine learning]
- https://arxiv.org/abs/2001.08889[Determination][Glass transition temperature][Polyimide][Atomistic molecular dynamics][Machine-learning algorithm]
- https://arxiv.org/abs/2001.10565[Classification][Strongly disordered topological wire][Machine learning]
- https://arxiv.org/abs/2001.11328[Accelerating continuum-scale brittle fracture simulation][Machine learning]
- https://arxiv.org/abs/2001.11322[Machine learning][Plastic deformation]
- https://arxiv.org/abs/2001.11153[Characterizing transition-metal dichalcogenide thin-film][Hyperspectral imaging][Machine learning]
- https://arxiv.org/abs/2312.05201[Embedding theory][ML][Real-time tracking][Structural dynamics][Hyperspectral dataset]
- https://arxiv.org/abs/2002.02994[Pairing glue][Cuprate superconductor][Self-energy][Machine learning]
- https://arxiv.org/abs/2002.05225[Accelerated design][Fe-based soft magnetic material][Machine learning][Stochastic optimization]
- https://arxiv.org/abs/1802.10127[Identifying melting Points of Metals][Classical MD][Unsupervised machine learning]
- https://arxiv.org/abs/2008.03275[Harnessing interpretable and unsupervised machine learning][Address Big Data][Modern X-ray diffraction]
- https://arxiv.org/abs/2007.08361[Unsupervised machine learning][Transfer learning][k-means clustering][Classify materials image data]
- https://arxiv.org/abs/2001.01711[Unsupervised machine learning][Band topology][PRL 124, 226401(2020)]
- https://arxiv.org/abs/2002.07266[Predicting new superconductors][Critical temperature][Unsupervised machine learning]
- https://arxiv.org/abs/2002.10004[Nanoscale electronic inhomogeneity][FeSe0.4Te0.6][Unsupervised machine learning]
- https://arxiv.org/abs/2005.10581[Spherical-angular dark field imaging][Sensitive microstructural phase clustering][Unsupervised machine learning]
- https://arxiv.org/abs/2010.09196[Unsupervised machine learning discovery][Chemical transformation pathway][Atomically-resolved imaging data]
- https://arxiv.org/abs/2101.05712[Unsupervised machine learning][Topological phase transition][Experimental data]
- https://arxiv.org/abs/2101.10161[Phase diagram study][Two-dimensional frustrated antiferromagnet][Unsupervised machine learning]
- https://arxiv.org/abs/2207.02075[Clustering superconductor][Unsupervised machine learning]
- https://arxiv.org/abs/2212.13550[Nanomaterial][Supercapacitor][Uncovering research theme][Unsupervised machine learning]
- https://arxiv.org/abs/2404.10943[Unsupervised machine learning][Detection][Exotic phase][Skyrmion phase diagram]
- https://arxiv.org/abs/2407.18320[Solving physics-based initial value problem][Unsupervised machine learning]
- https://arxiv.org/abs/2212.14087[Machine learning][Memory][Parent phase][Ferroelectric relaxor][Ba(Ti1−x,Zrx)O3]
- https://arxiv.org/abs/2004.10731[Unsupervised segmentation-based machine learning][Advanced analysis tool][Single molecule break junction data]
- https://arxiv.org/abs/2003.00994[Machine learning][Spectral indicator][Topology]
- https://arxiv.org/abs/2003.01878[Physics-informed feature engineering approach][Machine learning][Limited amounts of data][Alloy design][Shape memory alloy demonstration]
- https://arxiv.org/abs/2003.01943[Machine learning][Predicting and understanding][Mechanical property][Steel]
- https://arxiv.org/abs/2003.02722[Chemical bonding][Metallic Glass][Machine learning][Crystal orbital Hamilton population]
- https://arxiv.org/abs/2008.07694[False metal][Real insulator][Degenerate gapped metal]
- https://arxiv.org/abs/2008.07923[Learning DFT]
- https://arxiv.org/abs/2003.04550[Sampling strategy][Efficient potential energy surface mapping][Predicting atomic diffusivity][Machine learning]
- https://arxiv.org/abs/2003.11040[Machine learning][Quantum matter]
- https://arxiv.org/abs/2003.12081[Representation][Molecule][Material][Interpolation][Quantum-mechanical simulation][Machine learning]
- https://arxiv.org/abs/2003.13418[Machine learning][Discovery of application dependent design principles][Two-dimensional material]
- https://arxiv.org/abs/2003.13388[ML4Chem][Machine learning package][Chemistry][Materials Science]
- https://arxiv.org/abs/2004.00232[Machine learning][Multi-fidelity scale bridging][Dynamical simulation]
- https://arxiv.org/abs/2204.01788[Efficient machine-learning model][Fast assessment][Elastic properties][High-entropy alloy]
- https://arxiv.org/abs/2004.03025[Efficient computational design][2D van der Waals heterostructure][Band-alignment][Lattice-mismatch][Web-app generation][Machine-learning]
- https://arxiv.org/abs/2004.04439[Prediction][Mechanical property][Non-equiatomic high-entropy alloy][Atomistic simulation][Machine learning]
- https://arxiv.org/abs/1611.03277[Machine-learning based interatomic potential][Amorphous carbon]
- https://arxiv.org/abs/2004.04882[Machine-learning based extraction][Short-range part][Interaction][Non-contact atomic force microscopy]
- https://arxiv.org/abs/2404.08653[Machine-learning based selection][Synthesis][Candidate metal-insulator transition metal oxides]
- https://arxiv.org/abs/2208.12736[Machine-learning based screening][Lead-free halide double perovskite][Photovoltaic application]
- https://arxiv.org/abs/2211.07999[Machine-learning based prediction][Small molecule][Surface interaction potential]
- https://arxiv.org/abs/2409.12820[Machine-learning based high-bandwidth magnetic sensing]
- https://arxiv.org/abs/2004.05424[Machine learning][Coupling physics][Predict][High-temperatures alloy]
- https://arxiv.org/abs/2004.06703[Stacking fault energy prediction][Austenitic steel][Thermodynamic modeling][Machine learning]
- https://arxiv.org/abs/2209.12036[Temperature-dependent anharmonic phonon][Quantum paraelectric KTaO3][First principles][Machine-learned force field]
- https://arxiv.org/abs/2109.06282[Phase transition][Zirconia][Machine-learned force field][Beyond DFT]
- https://arxiv.org/abs/2302.12993[Complexity][Many-body interaction][Transition metal][Machine-learned force field][TM23 data set]
- https://arxiv.org/abs/2404.09755[Accurate quantum Monte Carlo force][Machine-learned force field][Ethanol][Benchmark]
- http://arxiv.org/abs/1609.05737[Machine-learned approximation][Density functional theory Hamiltonian]
- https://arxiv.org/abs/1903.09137[Modeling meso-scale energy localization][Shocked HMX][Part II][Training machine-learned surrogate model][Void shape][Void-void interaction]
- https://arxiv.org/abs/1906.02244[Machine-learned impurity level prediction][Semiconductor][Example of Cd-based chalcogenides]
- https://arxiv.org/abs/1911.11201[Machine-learned metrics][Predicting][likelihood of success][Materials discovery]
- https://arxiv.org/abs/2001.10591[Critical examination][Compound stability prediction][Machine-learned formation energy]
- https://arxiv.org/abs/2004.07295[Understanding high pressure hydrogen][Hierarchical machine-learned potential]
- https://arxiv.org/abs/2102.01103[Machine-learned phase diagram][Generalized Kitaev honeycomb magnet]
- https://arxiv.org/abs/2012.04489[alpha-beta phase transition][Zirconium][On-the-fly machine-learned force field]
- https://arxiv.org/abs/2303.00682[Nonlocal machine-learned exchange functional][Molecule][Solid]
- https://arxiv.org/abs/2307.08929[On-the-fly machine learning][Parametrization][Effective Hamiltonian]
- https://arxiv.org/abs/2404.07961[Overcoming][Chemical complexity bottleneck][On-the-fly machine learned molecular dynamics simulation]
- https://arxiv.org/abs/2104.03831[Machine-learned prediction][Electronic fields in a crystal]
- https://arxiv.org/abs/2112.03707[Critical assessment][Machine-learned repulsive potential][Density functional based tight-binding method][Case study for pure silicon]
- https://arxiv.org/abs/2004.08267[To switch or not to switch][Machine learning][Ferroelectricity]
- https://arxiv.org/abs/2004.08753[Machine learning][Metastable phase diagram]
- https://arxiv.org/abs/2004.08527[Large family][Two-dimensional ferroelectric metal][Discovered][Machine learning]
- https://arxiv.org/abs/2004.10091[Molecular design][Signal processing][Machine learning][Time-frequency-like representation][Forward design]
- https://arxiv.org/abs/2004.11817[Fast scanning probe microscopy][Machine Learning][Non-rectangular scan][Compressed sensing][Gaussian process optimization]
- https://arxiv.org/abs/2004.14766[Machine learning][Small dataset]
- https://arxiv.org/abs/2004.14407[Learning from Sparse Dataset][Predicting concrete's strength][Machine Learning]
- https://arxiv.org/abs/2004.14415[Revealing the phase diagram][Kitaev material][Machine Learning][Cooperation][Competition][Spin liquid]
- https://arxiv.org/abs/2005.00721[Learning What a Machine Learns][Many-body][Localization transition]
- https://arxiv.org/abs/2005.00951[Machine learning][Surrogate crystal plasticity model][Spatially resolved 3D orientation evolution][Uniaxial tension]
- https://arxiv.org/abs/2005.03595[Machine learning][Diffractive imaging][Subwavelength resolution]
- https://arxiv.org/abs/2005.05235[Machine learning][Materials development][Additive manufacturing]
- https://arxiv.org/abs/2005.04338[Multi-fidelity graph network][Machine learning][Experimental][Ordered and disordered materials]
- https://arxiv.org/abs/2005.04387[Eddy current testing][Metal crack][Spin Hall magnetoresistance sensor][Machine learning]
- https://arxiv.org/abs/2005.05831[Modelling][Dielectric constant][Machine learning]
- https://arxiv.org/abs/2005.08872[Prediction][Reduced glass transition temperature][Machine learning]
- https://arxiv.org/abs/2005.10210[Machine learning route][Band mapping][Band structure]
- https://arxiv.org/abs/2005.13046[Machine learning][Formation enthalpy][Intermetallics]
- https://arxiv.org/abs/2005.13265[Machine learning][Predicting][Time dependent dynamics][Local yielding][Dry foam]
- https://arxiv.org/abs/2005.14142[Dirac-type nodal spin liquid][Machine learning]
- https://arxiv.org/abs/2005.14260[Overview][Computer vision][Machine learning][Microstructural characterization and analysis]
- https://arxiv.org/abs/2005.14228[Machine learning][Condensed matter physics]
- https://arxiv.org/abs/2006.03320[Automatic machine-learning][Potential generation scheme][Simulation protocol][LiGePS-type superionic conductor]
- https://arxiv.org/abs/2006.04205[Machine learning dynamics][Phase separation][Correlated electron magnet]
- https://arxiv.org/abs/2006.10001[Investigating phase transition][Local crystallographic analysis][Machine learning][Atomic environment]
- https://arxiv.org/abs/2204.07230[Learning two-phase microstructure evolution][Neural operator][Autoencoder architecture]
- https://arxiv.org/abs/2006.10267[Machine learning][Chemical transformation][Si-graphene system][Atomically resolved image][Variational autoencoder]
- https://arxiv.org/abs/2107.08013[Machine-learning Kondo physics][Variational autoencoder]
- https://arxiv.org/abs/2401.11967[Exploring descriptor][Titanium microstructure][Digital fingerprint][Variational autoencoder]
- https://arxiv.org/abs/2212.13120[Neural structure field][Application][Crystal structure autoencoder]
- https://arxiv.org/abs/2303.18236[Physics][Chemistry][Parsimonious representation][Image analysis][Invariant variational autoencoder]
- https://arxiv.org/abs/2408.14928[Targetin][Partition function][Chemically disordered material][Generative approach][Inverse variational autoencoder]
- https://arxiv.org/abs/2311.17920[Autoencoder-based analytic continuation method][Strongly correlated quantum system]
- https://arxiv.org/abs/2407.04631[Autoencoder][Compressing angle-resolved photoemission spectroscopy data]
- https://arxiv.org/abs/2310.07927[Enhanced sampling][Crystal nucleation][Graph representation learnt variables]
- https://arxiv.org/abs/2309.06449[Quantized non-volatile nanomagnetic synapse][Autoencoder][Efficient unsupervised network anomaly detection]
- https://arxiv.org/abs/2006.11221[Leveraging machine learning][Alleviate Hubbard model][Sign problem]
- https://arxiv.org/abs/2405.14649[Leveraging machine learning][Advanced nanoscale X-ray analysis][Unmixing multicomponent signal][Enhancing chemical quantification]
- https://arxiv.org/abs/2006.13203[Machine learning][Active-nematic hydrodynamics]
- https://arxiv.org/abs/2006.14604[Opportunities and challenges][Machine Learning][Materials science][Annual Reviews of Materials Research, vol. 50, 2020]
- https://arxiv.org/abs/2006.14302[Database of 2D hybrid perovskite materials][Open-access collection][Crystal structure][Band gap][Atomic partial charge][Predicted by machine learning]
- https://arxiv.org/abs/2006.15898[Machine learning identification][Impurity][STM Image]
- https://arxiv.org/abs/2006.15227[Bypassing the computational bottleneck][Quantum-embedding theory][Strong electron correlation][Machine learning]
- https://arxiv.org/abs/2006.16833[Machine learning depinning][Dislocation pileup]
- https://arxiv.org/abs/2007.07531[Accelerating inverse crystal structure prediction][Machine learning][Carbon allotrope]
- https://arxiv.org/abs/2007.07512[Machine learning][Electron-boson mechanism][Superconductor]
- https://arxiv.org/abs/2007.10646[Accelerated mapping][Electronic density of states pattern][Metallic nanoparticle][Machine-learning]
- https://arxiv.org/abs/2007.12875[Fast evaluation][Interaction integral][Confined system][Machine learning]
- https://arxiv.org/abs/2007.13610[Analytic continuation][Self-energy][Machine Learning]
- https://arxiv.org/abs/2007.14206[Machine learning][Potential repository]
- https://arxiv.org/abs/2007.14832[Predicting][Molecular material][Multiscale simulation workflow][Machine learning]
- https://arxiv.org/abs/2007.14809[Machine learning phase][Criticality][Real data for training]
- https://arxiv.org/abs/1710.04187[Machine learning potential][Graphene]
- https://arxiv.org/abs/2307.10072[Efficient][Accurate][Transferable machine learning potential][Application][Dislocation][Cracks in Iron]
- https://arxiv.org/abs/2406.08275[Accurate and transferable machine learning interatomic potential][Equimolar and non-equimolar high-entropy diborides]
- https://arxiv.org/abs/1907.09088[Thermal conductivity modeling][Machine learning potential][Application to crystalline and amorphous silicon]
- https://arxiv.org/abs/2008.10773[Enabling robust offline active learning][Machine learning potential][Simple physics-based prior]
- https://arxiv.org/abs/1912.01789[Unravelling complex strengthening mechanism][NbMoTaW multi-principal element alloy][Machine learning potential]
- https://arxiv.org/abs/2212.14611[Complex dislocation network structure][Mixed small angle grain boundaries][Unraveled][Machine learning potential molecular dynamics]
- https://arxiv.org/abs/2006.05475[Simple and efficient algorithms][Training machine learning potential][Force data]
- https://arxiv.org/abs/2006.13655[Accurate][Transferable][Machine learning potential][Carbon]
- https://arxiv.org/abs/2007.11448[Machine learning potential][Hexagonal boron nitride][Thermally][Mechanically][Rippling]
- https://arxiv.org/abs/2007.15944[Application of machine learning potential][Predict][Grain boundary][fcc elemental metal]
- https://arxiv.org/abs/2008.09750[Machine learning potential][Multicomponent system][Ti-Al binary system]
- https://arxiv.org/abs/2008.10977[Bin and hash method][Analyzing reference data and descriptors][Machine Learning Potential]
- https://arxiv.org/abs/2105.11959[Structure and lattice thermal conductivity][Grain boundary][Silicon][Machine learning potential][Molecular dynamics]
- https://arxiv.org/abs/2107.02594[Accelerated identification][Equilibrium structure][Multicomponent inorganic crystal][Machine learning potential]
- https://arxiv.org/abs/2107.11311[AENET-LAMMPS][AENET-TINKER][Interface][Accurate][Efficient][Molecular dynamics simulation][Machine learning potential]
- https://arxiv.org/abs/2109.14068[Insight][Lithium manganese oxide-water interface][Machine learning potential]
- https://arxiv.org/abs/2112.10434[Machine learning potential][Always extrapolate][It does not matter]
- https://arxiv.org/abs/2201.01370[Interatomic machine learning potential][Aluminium][Application][Solidification phenomena]
- https://arxiv.org/abs/2201.07648[Convergence acceleration][Machine learning potential][Atomistic simulation]
- https://arxiv.org/abs/2202.13009[Long-range dispersion-inclusive machine learning potential][Structure search][Optimization][Hybrid organic-inorganic interface]
- https://arxiv.org/abs/2203.07219[Extending][Quantum computihttps://arxiv.org/abs/2203.09613ng][Materials science][Machine learning potential]
- https://arxiv.org/abs/2207.04009[Systematic structure dataset][Machine learning potential][Application][Moment tensor potential][Magnesium][Defect]
- https://arxiv.org/abs/2209.13823[Systematic development][Polynomial machine learning potential][Metallic][Alloy system]
- https://arxiv.org/abs/2401.14877[Predictive power][Polynomial machine learning potential][Liquid state][22 elemental systems]
- https://arxiv.org/abs/2401.17531[On-the-fly training][Polynomial machine learning potential][Computing lattice thermal conductivity]
- https://arxiv.org/abs/2403.02570[Globally-stable][Metastable crystal structure enumeration][Polynomial machine learning potential][Elemental As, Bi, Ga, In, La, P, Sb, Sn, and Te]
- https://arxiv.org/abs/2407.20630[Polynomial machine learning potential][Application][Global structure search][Ternary Cu-Ag-Au alloy]
- https://arxiv.org/abs/2210.01378[Lattice dynamics][Elastic properties][alpha-U][High-temperature][High-pressure][Machine learning potential simulation]
- https://arxiv.org/abs/2211.05713[Simulation][Machine learning potential][Identify][Ion conduction mechanism][Mediating non-Arrhenius behavior][LGPS]
- https://arxiv.org/abs/2301.03497[Phase transition][Inorganic halide perovskite][Machine learning potential]
- https://arxiv.org/abs/2306.03989[Machine learning potential-based generative algorithm][On-lattice crystal structure prediction]
- https://arxiv.org/abs/2306.07246[Reliable machine learning potential][Artificial neural network][Graphene]
- https://arxiv.org/abs/2307.15528[Machine learning potential][Modelling H2 Adsorption/Diffusion][MOF][Open metal site]
- https://arxiv.org/abs/2307.15127[Unravelling negative][In-plane stretchability][2D MOF][Large scale machine learning potential molecular dynamics]
- https://arxiv.org/abs/2309.01089[Vacancy ordering][Boson peak][Metastable cubic Ge-Sb-Te][Machine learning potential]
- https://arxiv.org/abs/2310.19350[Disorder-dependent Li diffusion][Li6PS5Cl investigated][Machine learning potential]
- https://arxiv.org/abs/2311.04461[Hydrogen diffusion][Lower mantle][Revealed][Machine learning potential]
- https://arxiv.org/abs/2401.06622[Capturing short-range order][High-entropy alloy][Machine learning potential]
- https://arxiv.org/abs/2401.17875[Perspective][Atomistic simulation][Water and aqueous systems][Machine learning potential]
- https://arxiv.org/abs/2402.08834[Machine learning potential][Powered insight][Mechanical stability][Amorphous Li-Si alloy]
- https://arxiv.org/abs/2403.02155[Accelerating fourth-generation machine learning potential][Quasi-linear scaling particle mesh charge equilibration]
- https://arxiv.org/abs/2403.05724[From electrons to phase diagrams][Classical and machine learning potentials][Automated workflow][Materials science][Pyiron]
- https://arxiv.org/abs/2405.00306[Environment-adaptive machine learning potential]
- https://arxiv.org/abs/2406.00183[Predicting solvation free energies][Implicit solvent machine learning potential]
- https://arxiv.org/abs/2406.07157[Machine learning potential][Cu-W system]
- https://arxiv.org/abs/2406.08243[Efficient strategy][Construct general machine learning potential][High-entropy ceramics]
- https://arxiv.org/abs/2409.20235[Gegeneral machine learning model][Aluminosilicate melt viscosity][Application][Surface properties][Dry lava planet]
- https://arxiv.org/abs/2406.13178[Efficient modelling][Anharmonicity and quantum effects][PdCuH2][Machine learning potential]
- https://arxiv.org/abs/2406.17499[Evaluating][Improving][Predictive accuracy][Mixing enthalpies][Volume][Disordered alloy][Universal pre-trained machine learning potential]
- https://arxiv.org/abs/2407.21088[Hydrogen diffusion][Magnesium][Machine learning potential]
- https://arxiv.org/abs/2407.21088[Hydrogen diffusion][Magnesium][Machine learning potential]
- https://arxiv.org/abs/2408.13416[Accelerating material melting temperature prediction][Implementing machine learning potential][SLUSCHI package]
- https://arxiv.org/abs/2408.16157[Importance of learning without Constraints][Reevaluating benchmark][Invariant and equivariant features][Machine learning potential][Generating free energy landscape]
- https://arxiv.org/abs/2402.03219[Experiment-driven atomistic materials modeling][Case study][Combining][XPS][ML potential][Oxygen-rich amorphous carbon]
- https://arxiv.org/abs/2406.12909[Scalable training][Graph foundation model][Atomistic materials modeling][HydraGNN]
- https://arxiv.org/abs/2408.04765[Scalable learning][Potential][Predict time-dependent Hartree-Fock dynamics]
- https://arxiv.org/abs/2008.01243[Predicting][Activity][Selectivity][Bimetallic metal catalyst][Ethanol Reforming][Machine learning]
- https://arxiv.org/abs/2008.03189[Assessment of the structural resolution][Various fingerprints][Machine learning]
- https://arxiv.org/abs/2008.05125[Prediction][Rare-earth 2-17-X Magnets Ce2Fe17-xCoxCN][Combined machine-learning and ab-initio Study]
- https://arxiv.org/abs/2302.08101[GAASP][Genetic algorithm based atomistic sampling protocol][High-entropy material]
- https://arxiv.org/abs/2008.09187[Designing][Optical glass][Machine learning][Genetic algorithm]
- https://arxiv.org/abs/2008.09681[Unifying framework][Strong and fragile liquid][Machine learning][[Liquid silica]
- https://arxiv.org/abs/2008.11213[Engineering topological phase][Statistical and machine learning method]
- https://arxiv.org/abs/2008.11277[Detecting non-local effect][Simple covalent system][Machine learning method]
- https://arxiv.org/abs/2008.12412[Predicting band gap][Band-edge position][Oxide perovskite][DFT][Machine learning]
- https://arxiv.org/abs/2009.03194[Predicting thermal, mechanical, and optical properties][Oxide glass][Machine learning][Large dataset]
- https://arxiv.org/abs/2209.00658[Stable solid molecular hydrogen][Above 900K][Machine-learned potential trained][Diffusion Quantum Monte Carlo][Phys. Rev. Lett. 130, 076102 (2023)]
- https://arxiv.org/abs/2009.05050[Charting][Low-loss region][Electron energy loss spectroscopy][Machine learning]
- https://arxiv.org/abs/2009.06476[Machine learning][Non-Hermitian topological phase]
- https://arxiv.org/abs/2009.08501[Separating physically distinct mechanism][Complex infrared plasmonic nanostructure][Machine learning][Electron energy loss spectroscopy]
- https://arxiv.org/abs/2009.11569[IrO2 surface complexion][Identified][Machine learning][Surface Investigation]
- https://arxiv.org/abs/2009.12082[Probing][Transition][Dislocation jamming][Pinning][Machine learning]
- https://arxiv.org/abs/2010.00397[Upscaling][Porosity-permeability relationship][Microporous carbonate][Darcy scale][Machine learning]
- https://arxiv.org/abs/2010.01030[Machine-learning-enhanced][Time-of-flight mass spectrometry analysis]
- https://arxiv.org/abs/2010.03702[Quantitative prediction][Photo-emission dynamics][Metal halide perovskite][Machine learning]
- https://arxiv.org/abs/2010.04815[Data-centric framework][Crystal structure identification][Atomistic simulation][Machine learning]
- https://arxiv.org/abs/2010.07683[Potential][Challenge][Polymer informatics][Exploiting machine learning][Polymer design]
- https://arxiv.org/abs/2010.08151[Multiscale][Nanoconfined charging dynamics][Supercapacitor][Machine learning]
- https://arxiv.org/abs/2010.09438[How machine learning can help the design and analysis][Composite materials][Structure]
- https://arxiv.org/abs/2010.09435[Introduction to electrocatalyst design][Machine learning][Renewable energy storage]
- https://arxiv.org/abs/2010.14048[MeltNet][Predicting alloy melting temperature][Machine learning]
- https://arxiv.org/abs/2010.16099[MLatticeABC][Generic lattice constant prediction][Crystal material][Machine learning]
- https://arxiv.org/abs/2011.00383[Machine learning forecasting][Active nematics]
- https://arxiv.org/abs/2011.01262[Structural signature][Thermodynamic stability][Vitreous silica][Insight][[Machine Learning][Molecular dynamics simulation]
- https://arxiv.org/abs/2011.02259[Accelerating coupled cluster calculation][Nonlinear dynamics][Shallow machine learning]
- https://arxiv.org/abs/2011.02038[Machine learning][Evolutionary prediction][Superhard B-C-N compound]
- https://arxiv.org/abs/2011.03861[Supervised learning][Physical network][From machine learning to learning machines]
- https://arxiv.org/abs/2203.09788[Orientation adaptive minimal learning machine][Application][Thiolate-protected gold nanocluster][Gold-thiolate ring]
- https://arxiv.org/abs/2404.08657[Advancing extrapolative prediction][Material properties][Learning to learn]
- https://arxiv.org/abs/2011.06900[Evolutionary computing][Machine learning][Discovering][Low-energy defect configuration]
- https://arxiv.org/abs/2011.09458[Machine learning][Phase behavior][Active matter system]
- https://arxiv.org/abs/2012.07583[Machine learning][Data analytics][Design and manufacturing][High-entropy material][Exhibiting mechanical][Fatigue]
- https://arxiv.org/abs/2012.08653[Optimization][Quantum-dot qubit fabrication][Machine learning]
- https://arxiv.org/abs/2012.11841[Residual matrix product state][Machine learning]
- https://arxiv.org/abs/2012.12244[Machine learning dielectric screening][Simulation][Excited state][Molecule][Material]
- https://arxiv.org/abs/2012.15222[Global optimization][Atomistic structure][Enhanced][[Machine learning]
- https://arxiv.org/abs/2101.00269[Extending Shannon's ionic radii database][Machine learning]
- https://arxiv.org/abs/2101.01942[Machine-learning free-energy functional][Density profile][Simulation]
- https://arxiv.org/abs/2011.10719[Predicting impurity spectral function][Machine learning]
- https://arxiv.org/abs/2101.02810[Machine-learning-guided prediction model][Critical temperature][Cuprate]
- https://arxiv.org/abs/2101.04383[Interpretable discovery][New semiconductor][Machine learning]
- https://arxiv.org/abs/2101.06307[Machine-learning][Accelerated geometry optimization][Molecular simulation]
- https://arxiv.org/abs/2101.06385[Mixed precision][Fermi-operator expansion][Tensor Core][Machine learning perspective]
- https://arxiv.org/abs/2101.07460[High temperature oxidation behavior][Disordered (Ti0.5Zr0.5)2AlC MAX phase][Machine learning-augmented DFT Approach]
- https://arxiv.org/abs/2101.09432[Machine learning][Dynamics of quantum kicked rotor]
- https://arxiv.org/abs/2102.00173[Determination][Dzyaloshinskii-Moriya interaction][Pattern recognition][Machine learning]
- https://arxiv.org/abs/2102.03024[Machine learning][Neutron and X-ray scattering]
- https://arxiv.org/abs/2102.09714[Machine learning][Mirror skin effect][Presence of disorder]
- https://arxiv.org/abs/2103.00193[High-Tc ternary metal hydride][YKH12][LaKH12][Discovered][Machine learning]
- https://arxiv.org/abs/1912.12923[Bayesian tensor network][Optimization algorithm][Probabilistic machine learning]
- https://arxiv.org/abs/2410.06422[Predicting battery capacity fade][Probabilistic machine learning model][With and without pre-trained priors]
- https://arxiv.org/abs/2304.05949[CMOS][Stochastic nanomagnet][Heterogeneous computer][Probabilistic inference and learning]
- https://arxiv.org/abs/1904.03160[Discrete Fourier transform][Prediction][Molecule][Quantum machine learning]
- https://arxiv.org/abs/1907.03741[Expressive power][Tensor-network factorization][Probabilistic modeling][Hidden Markov model][Quantum machine learning]
- https://arxiv.org/abs/2002.12925[Predicting excited states][Ground state wavefunction][Supervised quantum machine learning]
- https://arxiv.org/abs/2004.12076[Quantum machine learning][Quantum biomimetics][Perspective]
- https://arxiv.org/abs/2103.02037[Implementation][Quantum machine learning][Electronic structure calculation][Periodic system][Quantum computing device]
- https://arxiv.org/abs/2111.05076[Application][Quantum machine learning][Quantum correlated system][Quantum convolutional neural network][Classifier][Many-body wavefunction][Quantum variational eigensolver]
- https://arxiv.org/abs/2203.15525[Quantum machine learning correct][Classical force field][Stretching DNA base pairs][Explicit solvent]
- https://arxiv.org/abs/2405.18989[Classification analysis][Transition-metal chalcogenides and oxides][Quantum machine learning]
- https://arxiv.org/abs/2409.07405[Uncovering quantum many-body scars][Quantum machine learning]
- https://arxiv.org/abs/2409.16346[Scalable quantum dynamics compilation][Quantum machine learning]
- https://arxiv.org/abs/2103.04169[Free energy][Machine-learning-enhanced ab initio calculation][(CoxMn1-x)3O4 mixed phase][Phys. Rev. Materials 5, 035402, 2021]
- https://arxiv.org/abs/2103.05510[Machine-learning semi-local density functional theory][Many-body lattice model][Zero and finite temperature]
- https://arxiv.org/abs/2103.07236[Machine learning quantum criticality][Spin-1/2 quantum antiferromagnet][Square lattice][Plaquette structure]
- https://arxiv.org/abs/2103.07513[Predicting][Phase preference][Two-dimensional transition metal dichalcogenide][Machine learning]
- https://arxiv.org/abs/2103.12662[Comprehensive quality investigation][Wire-feed additive manufacturing][Machine learning][Experimental data]
- https://arxiv.org/abs/2204.04198[Modern application][Machine learning][Quantum science]
- https://arxiv.org/abs/2406.03121[MESS][Modern electronic structure simulation][Machine learning]
- https://arxiv.org/abs/2309.01160[Oxygen vacancy formation energy][Metal oxide][High throughput computational studies][Machine learning prediction]
- https://arxiv.org/abs/1910.01183[High-throughput DFPT][Machine learning prediction][Infrared][Piezoelectric][Dielectric Response]
- https://arxiv.org/abs/1801.08217[Electronic branched Flow][Random potential][Machine learning prediction]
- https://arxiv.org/abs/1809.01753[Automatic convergence][Machine learning prediction][Monkhorst-Pack k-points][Plane-wave cut-off][DFT]
- https://arxiv.org/abs/1812.11642[Machine learning prediction][DNA charge transport]
- https://arxiv.org/abs/1906.03233[Machine learning prediction][Accurate atomization energy][Organic molecule][Low-fidelity quantum chemical calculation]
- https://arxiv.org/abs/2012.15739[Uncertainty bound][Multivariate machine learning prediction][High-strain brittle fracture]
- https://arxiv.org/abs/2103.12543[Machine learning prediction][Magnetic][Fe-based metallic glass][Local structure]
- https://arxiv.org/abs/2108.08766[Molecular simulation-derived feature][Machine learning prediction][Metal glass forming ability]
- https://arxiv.org/abs/2109.13762[Machine learning prediction][Superalloy microstructure]
- https://arxiv.org/abs/2204.05967[Machine learning prediction][Local electronic properties][Disordered correlated electron system]
- https://arxiv.org/abs/2210.13597[Critical examination][Robustness][Generalizability][Machine learning prediction][Materials properties]
- https://arxiv.org/abs/2304.01146[Distance-based analysis][Machine learning prediction reliability][Dataset][Materials science]
- https://arxiv.org/abs/2304.02218[Identification][High-reliability region][Machine learning prediction][Materials science][Transparent conducting oxides and perovskites as examples]
- https://arxiv.org/abs/2305.15390[Machine learning prediction][Critical cooling rate][Metallic glasses][Expanded Dataset][Elemental feature][Chemistry of Materials, 34(7), 2945-2954 (2022)]
- https://arxiv.org/abs/2307.06879[Machine learning prediction][High-Curie-Temperature Material]
- https://arxiv.org/abs/2312.02475[Accurate machine learning prediction][Coercivity][High-performance permanent magnet]
- https://arxiv.org/abs/2401.10998[Leveraging domain adaptation][Accurate machine learning prediction][New halide Perovskites]
- https://arxiv.org/abs/2310.04667[Machine learning prediction][Self-assembly][Analysis][Molecular structure dependence][Critical packing parameter]
- https://arxiv.org/abs/2404.13858[Machine learning prediction model][Solid electrolytes][Lattice dynamics properties]
- https://arxiv.org/abs/2103.12066[Machine learning][In situ quality estimation][Molten pool condition-quality relation][Modeling][Experimental data]
- https://arxiv.org/abs/2103.15998[Revealing][Chemical bonding][Adatoms array][Machine learning][3D scanning tunneling spectroscopy data]
- https://arxiv.org/abs/2104.00586[Machine learning enabled prediction][Cathode material][Zn ion Battery]
- https://arxiv.org/abs/2104.05786[Understanding fission gas bubble distribution][Lanthanide transportation][Thermal conductivity degradation][Neutron-irradiated alpha-U][Machine learning]
- https://arxiv.org/abs/2104.08318[LixCoO2 phase stability][Machine learning-enabled scale bridging][Electronic structure][Statistical mechanics][Phase field theory]
- https://arxiv.org/abs/2104.10888[Machine learning aided materials design platform][Predicting][Mechanical property][Na-ion solid-state electrolyte]
- https://arxiv.org/abs/2104.12921[Machine learning][Phase transition][Nonlinear polariton lattice]
- https://arxiv.org/abs/2105.05697[Systematic coarse-graining][Epoxy resin][Machine learning-informed energy renormalization]
- https://arxiv.org/abs/2105.07303[Composition based crystal materials symmetry prediction][Machine learning][Enhanced descriptor]
- https://arxiv.org/abs/2105.08221[Arrested phase separation][Double-exchange model][Machine-learning][Large-scale simulation][Phys. Rev. Lett. 127, 146401(2021)]
- https://arxiv.org/abs/2105.09729[Adoption][Image-driven machine learning][Microstructure characterization][Materials Design][Perspective]
- https://arxiv.org/abs/2105.09947[Ground-state][Machine learning][Quantum constraint]
- https://arxiv.org/abs/2105.11319[Machine learning][Feature engineering][Crystal structure][Application][Prediction][Formation energy][Cubic compound]
- https://arxiv.org/abs/2105.12943[Phase transition][Two-dimensional ferroelectric][Paraelectric][Ga2O3 monolayer][Density functional theory][Machine-learning Study]
- https://arxiv.org/abs/2105.12867[Application][DFTB][Machine learning][Evaluate the stability][Biomass intermediate][Rh(111) surface]
- https://arxiv.org/abs/2105.14806[Aluminium alloy design][Discovery][Machine learning]
- https://arxiv.org/abs/2106.04229[BIGDML][Exact machine learning][Force field]
- https://arxiv.org/abs/2106.04162[Glue function][Cuprate][Optical spectra][Machine learning]
- https://arxiv.org/abs/2106.05235[Machine learning][Superconducting critical temperature][Eliashberg theory]
- https://arxiv.org/abs/2106.07749[Machine learning][Composition-property relationship][Chalcogenide glass]
- https://arxiv.org/abs/2106.10951[Discovering equation][Govern experimental materials stability][Environmental stress][Scientific machine learning]
- https://arxiv.org/abs/2106.13109[Machine learning][Tame divergent density functional approximation][New path][Consensus materials design principles]
- https://arxiv.org/abs/2106.13327[Machine learning][Data mining][Leverage community knowledge][Engineering][Stable metal-organic framework]
- https://arxiv.org/abs/2106.16075[Machine learning][Derivative discontinuity][Density-functional theory]
- https://arxiv.org/abs/2106.16152[Machine learning][S-Wave scattering][Phase shift][Bypassing][Radial schrödinger equation]
- https://arxiv.org/abs/2107.00493[Graph-based machine learning][Beyond stable material][Relaxed crystal structure]
- https://arxiv.org/abs/2107.01055[Atomic structure optimization][Machine-learning][Interpolation][Chemical element][Phys. Rev. Lett. 127, 166001 (2021)]
- https://arxiv.org/abs/2107.01040[Machine learning][Microscopic ingredient][Graphene oxide/cellulose interaction]
- https://arxiv.org/abs/2107.03735[Lattice thermal conductivity][Half-Heusler][DFT][Machine learning][Enhancing predictivity][Active sampling][Principal component analysis]
- https://arxiv.org/abs/1808.02470[Machine learning-assisted discovery][Many new solid Li-ion conducting materials]
- https://arxiv.org/abs/2107.02613[Machine learning-assisted][High-throughput][Semi-empirical search][OFET][Molecular material]
- https://arxiv.org/abs/2109.02794[Machine learning-assisted exploration][Thermally conductive polymer][High-throughput MD]
- https://arxiv.org/abs/2201.11168[Machine learning-assisted design][Material properties]
- https://arxiv.org/abs/2305.15410[Machine learning-assisted close-set X-ray diffraction phase identification][Transition metal]
- https://arxiv.org/abs/2208.03410[Machine learning-assisted manipulation][Readout][Molecular spin qubit]
- https://arxiv.org/abs/2309.16415[Interpreting X-ray absorption spectra][Vanadyl phthalocyanine][Spin qubit candidate][Machine learning-assisted approach]
- https://arxiv.org/abs/2407.01145[Machine learning-assisted 3D printing][Thermoelectric material][Ultrahigh performance][Room temperature]
- https://arxiv.org/abs/2409.00360[Accelerating phonon thermal conductivity prediction][Order of magnitude][Machine learning-assisted extraction][Anharmonic force constant]
- https://arxiv.org/abs/2107.07028[Machine learning][Materials discovery][Two-dimensional topological insulator]
- https://arxiv.org/abs/2107.08919[Data cluster analysis][Machine learning][Classification][Twisted bilayer graphene]
- https://arxiv.org/abs/2107.10387[Design][Graphical user interface][Few-shot machine learning classification][Electron microscopy data]
- https://arxiv.org/abs/2404.00155[Machine learning classification][Local environments][Molecular crystal]
- https://arxiv.org/abs/2107.10215[Comprehensive study][Lithium adsorption][diffusion][Janus Mo/WXY (X, Y= S,Se, Te)][Machine learning]
- https://arxiv.org/abs/2107.12975[Cross-architecture tuning][Silicon and SiGe-based quantum devices][Machine learning]
- https://arxiv.org/abs/2107.13960[Data-driven dynamical mean-field theory][Error-correction approach][Solve][Quantum many-body problem][Machine learning]
- https://arxiv.org/abs/2209.14328[Scalably learning][Quantum many-body Hamiltonian][Dynamical data]
- https://arxiv.org/abs/2107.14664[Distributed representation][Atoms and Materials][Machine learning]
- https://arxiv.org/abs/2107.14362[MLMOD Package][Machine learning][Data-driven modeling][LAMMPS]
- https://arxiv.org/abs/2107.14280[Deciphering cryptic behavior][Bimetallic transition metal complex][Machine learning]
- https://arxiv.org/abs/2108.03523[KLIFF][Framework][Develop analytic][Machine learning][Interatomic potential]
- https://arxiv.org/abs/2108.04809[Spiderweb nanomechanical resonator][Bayesian optimization][Nature and guided][Machine learning]
- https://arxiv.org/abs/2108.04904[Machine learning][Consistent thermodynamic model][Automatic differentiation]
- https://arxiv.org/abs/2108.05823[Machine-learning detection][Berezinskii-Kosterlitz-Thouless transition][q-state clock model][Phys. Rev. B 104, 075114 (2021)]
- https://arxiv.org/abs/1804.02150[Automatic selection][Atomic fingerprint][Reference configuration][Machine-learning potential]
- https://arxiv.org/abs/2110.00624[Ultra-fast][Interpretable machine-learning potential]
- https://arxiv.org/abs/1810.10820[Impact of local lattice relaxations][Phase stability][Chemical ordering][bcc NbMoTaW][High-entropy alloy][Machine-learning potential]
- https://arxiv.org/abs/2008.07786[Training machine-learning potential][Crystal structure prediction][Disordered structure]
- https://arxiv.org/abs/2101.10468[Construction of machine-learning potential][Accurate and efficient atomic-scale simulation]
- https://arxiv.org/abs/2105.00414[Predicting highly correlated hydride-ion diffusion][SrTiO3 crystal][Fragment kinetic Monte Carlo method][Machine-learning potential]
- https://arxiv.org/abs/2107.05620[Machine-learning potential][Enable predictive][tractable][High-throughput screening][Random alloy]
- https://arxiv.org/abs/2108.06232[Generalizable machine-learning potential][Ag-Au nanoalloy][Surface reconstruction][Segregation][Diffusion]
- https://arxiv.org/abs/2207.09010[Short-range order][Its impact][BCC NbMoTaW multi-principal element alloy][Machine-learning potential]
- https://arxiv.org/abs/2206.13727[Persistent homology-based descriptor][Machine-learning potential]
- https://arxiv.org/abs/2206.05068[Boosting current-induced molecular dynamics][Machine-learning potential]
- https://arxiv.org/abs/2202.13773[Spectral-neighbour representation][Vector field][Machine-learning potential][Including spin]
- https://arxiv.org/abs/2208.00804[Efficient atomistic simulation][Radiation damage][W and W-Mo][Machine-learning potential]
- https://arxiv.org/abs/2209.00316[Evolution][Boroxol ring][Lithium borosilicate glasses][Machine-learning potential]
- https://arxiv.org/abs/2212.10385[High-accuracy thermodynamic properties][Melting point][Ab initio calculation][Machine-learning potential]
- https://arxiv.org/abs/2208.02974[Origin of negative thermal expansion][Pressure induced amorphization][Zirconium tungstate][Machine-learning potential]
- https://arxiv.org/abs/2404.17353[Large-scale atomistic study][Plasticity][Amorphous gallium oxide][Machine-learning potential]
- https://arxiv.org/abs/2309.00996[Machine-learning potential][Nanoscale simulation][Deformation][Fracture][Example of TiB2 ceramic]
- https://arxiv.org/abs/2303.14434[Heat flux][Semi-local machine-learning potential]
- https://arxiv.org/abs/2406.04948[Origin of the yield stress anomaly][L12 intermetallics][Physically-informed machine-learning potential]
- https://arxiv.org/abs/2408.05782[Accurate prediction][Structural and mechanical properties][Amorphous material][Machine-learning potential][Case study of silicon nitride]
- https://arxiv.org/abs/2409.08886[PiNNAcLe][Adaptive learn-on-the-fly algorithm][Machine-learning potential]
- https://arxiv.org/abs/2310.13756[Learning interatomic potential][Multiple scale]
- https://arxiv.org/abs/1706.10229[Achieving DFT accuracy][Machine-learning interatomic potential][Thermomechanics][Defects][Bcc ferromagnetic iron]
- https://arxiv.org/abs/1908.07330[Machine-learning interatomic potential][Radiation damage][Defect][Tungsten]
- https://arxiv.org/abs/2006.06794[Machine-learning interatomic potential][Enable first-principles multiscale modeling][Lattice thermal conductivity][Graphene/Borophene heterostructure]
- https://arxiv.org/abs/2009.03662[Accelerating first-principles estimation][Thermal conductivity][Machine-learning interatomic potential][MTP/ShengBTE solution]
- https://arxiv.org/abs/2102.06163[Machine-learning interatomic potential][Materials science]
- https://arxiv.org/abs/2203.01117[Machine-learning interatomic potential][Molecular dynamics simulation][Ferroelectric KNbO3 perovskite]
- https://arxiv.org/abs/2110.10434[Exploring thermal expansion][Carbon-based nanosheet][Machine-learning interatomic potential]
- https://arxiv.org/abs/2212.03096[Complex Ga2O3 polymorph][Explored][Accurate][General-purpose machine-learning interatomic potential]
- https://arxiv.org/abs/2009.06533[Development][General-purpose machine-learning interatomic potential][Physically-informed neural network method]
- https://arxiv.org/abs/2302.08698[Complex strengthening mechanism][Nanocrystalline Ni-Mo alloy][Machine-learning interatomic potential]
- https://arxiv.org/abs/2403.05729[Systematic assessment][Various universal machine-learning interatomic potential]
- https://arxiv.org/abs/2407.04126[Atomistic modeling][Bulk and grain boundary diffusion][Solid electrolyte][Li6PS5Cl][Machine-learning interatomic potential]
- https://arxiv.org/abs/2407.10361[Information-entropy-driven generation][Material-agnostic dataset][Machine-learning interatomic potential]
- https://arxiv.org/abs/2408.06322[Discovering high-entropy oxide][Machine-learning interatomic potential]
- https://arxiv.org/abs/2409.06982[Comparison of intermediate-range order][GeO2 glass][Molecular dynamics][Machine-learning interatomic potential][Reverse Monte Carlo fitting][Experimental data]
- https://arxiv.org/abs/2409.13390[Hydrogen under pressure][Benchmark][Machine-learning interatomic potential]
- https://arxiv.org/abs/2405.20270[Bridging electronic and classical density-functional theory][Universal machine-learned functional approximation]
- https://arxiv.org/abs/1802.07605[Accelerating crystal structure prediction][Machine-learning][Interatomic potential][Active learning]
- https://arxiv.org/abs/2108.10601[Non-classical nucleation][Zinc oxide][Physically-motivated machine-learning approach]
- https://arxiv.org/abs/2108.12945[Machine learning][Predicting thermal transport properties of Solids]
- https://arxiv.org/abs/2306.02091[Sub-micrometer phonon mean free path][Metal-organic framework][Revealed][Machine-learning molecular dynamics simulation]
- https://arxiv.org/abs/2109.01501[Order-N orbital-free density-functional calculation][Machine learning][Functional derivative][Semiconductor][Metal]
- https://arxiv.org/abs/2109.08005[Elucidating proximity magnetism][Polarized neutron reflectometry][Machine learning]
- https://arxiv.org/abs/2109.09394[Prediction][Metal alloy][Machine learning]
- https://arxiv.org/abs/2110.00517[Prediction][Carbon nanostructure][Mechanical][Defect][Machine learning]
- https://arxiv.org/abs/2110.00997[Deep dive][Machine learning DFT][Materials Science and Chemistry]
- https://arxiv.org/abs/2110.04115[Machine-learning][Determination][Stacking order][Bilayer graphene]
- https://arxiv.org/abs/2110.03923[Machine learning][Accelerate halide perovskite commercialization][Scale-Up]
- https://arxiv.org/abs/2110.04529[Design][Quaternary eutectic solder][Machine learning]
- https://arxiv.org/abs/2110.06818[Assessing][Accuracy][Machine learning thermodynamic perturbation theory][DFT and Beyond]
- https://arxiv.org/abs/2110.08284[Machine learning][Assisted][GaAsN circular polarimeter]
- https://arxiv.org/abs/2110.10564[Multiscale materials modelling][Machine learning][Hydrogen-steel interaction][Deformation]
- https://arxiv.org/abs/2110.15241[Photonic kernel machine learning][Ultrafast spectral analysis]
- https://arxiv.org/abs/2110.15817[Integration][Machine learning][Neutron scattering][Hamiltonian tuning][Spin ice][Pressure]
- https://arxiv.org/abs/2206.09944[Integrating machine learning][Mechanistic model][Predicting][Yield strength][High entropy alloy]
- https://arxiv.org/abs/2111.01037[Interpretable][Explainable][Machine learning][Materials Science][Chemistry]
- https://arxiv.org/abs/2111.01111[Predicting][Hard-coating alloy][Ab-initio][Machine learning method]
- https://arxiv.org/abs/2111.00851[Quantum computing enhanced machine learning][Physico-chemical application]
- https://arxiv.org/abs/2111.01905[Audacity][huge][Overcoming challenge][Data scarcity][Data quality][Machine learning][Computational materials discovery]
- https://arxiv.org/abs/2111.02206[Machine-learning correction][Density-functional crystal structure optimization]
- https://arxiv.org/abs/2111.06496[Uncovering material deformation][Machine learning][Combined][Four-dimensional scanning transmission electron microscopy]
- https://arxiv.org/abs/2111.07362[Machine learning][Strain-rate-dependent predictability][Discrete dislocation plasticity]
- https://arxiv.org/abs/2111.09368[Machine learning][Compositional disorder][Comparison][Different descriptors]
- https://arxiv.org/abs/2111.11285[Bridging][Reality gap][Quantum device][Physics-aware machine learning]
- https://arxiv.org/abs/2409.12877[Physics aware machine learning][Micromagnetic energy minimization][Recent algorithmic development]
- https://arxiv.org/abs/2111.11262[Synergistic coupling][Ab initio-machine learning simulation][Dislocation]
- https://arxiv.org/abs/2405.05092[Understanding solid nitrogen][Machine learning simulation]
- https://arxiv.org/abs/2111.12547[Modified divide-and-conquer based machine learning method][Predicting][Creep life][Superalloy]
- https://arxiv.org/abs/2111.12923[Discovering superhard B-N-O compound][Iterative machine learning][Evolutionary structure prediction]
- https://arxiv.org/abs/2111.15177[Machine learning study][Flat-Band state][Constructed][Molecular-orbital representation][Randomness][J. Phys. Soc. Jpn. 91, 044703 (2022)]
- https://arxiv.org/abs/2112.02169[UV-visible absorption spectra][Solvated molecule][Quantum chemical machine learning]
- https://arxiv.org/abs/2112.08676[Machine learning-accelerated computational solid mechanics][Application][Linear elasticity]
- https://arxiv.org/abs/2112.09612[Inorganic synthesis][Reaction condition prediction][Generative machine learning]
- https://arxiv.org/abs/2112.09764[Reflection][Future of machine learning][Materials research]
- https://arxiv.org/abs/2112.10789[Machine learning discovery][New phases][Programmable quantum simulator snapshot]
- https://arxiv.org/abs/2112.11099[High pressure hydrogen][Machine learning][Quantum Monte Carlo]
- https://arxiv.org/abs/2112.12124[Machine learning nonequilibrium electron force][Adiabatic spin dynamics]
- https://arxiv.org/abs/2012.07578[Thermal conductivity][h-BN monolayer][Machine learning interatomic potential]
- https://arxiv.org/abs/2201.08906[Machine learning interatomic potential][High throughput screening][Optimization][High-entropy alloy]
- https://arxiv.org/abs/1901.02118[Group-theoretical high-order rotational invariant][Structural representation][Application][Linearized machine learning interatomic potential]
- https://arxiv.org/abs/2301.08630[Evaluating approach][On-the-fly machine learning interatomic potential][Activated mechanisms sampling][Activation-relaxation technique nouveau]
- https://arxiv.org/abs/2005.04913[Exploring phononic][Two-dimensional material][Machine learning interatomic potential]
- https://arxiv.org/abs/2006.01915[Sensitivity][Dimensionality][Atomic environment representation][Machine learning interatomic potential]
- https://arxiv.org/abs/2308.15653[Statistical method][Resolving poor uncertainty quantification][Machine learning interatomic potential]
- https://arxiv.org/abs/2307.13710[Robust training][Machine learning interatomic potential][Dimensionality reduction][Stratified sampling]
- https://arxiv.org/abs/2306.11639[Discrepancies][Error evaluation metrics][Machine learning interatomic potential]
- https://arxiv.org/abs/2212.00263[Lattice thermal conductivity][Elastic modulus][XN4 (X=Be, Mg and Pt) 2D materials][Machine learning interatomic potential]
- https://arxiv.org/abs/2209.12322[Classical][Machine learning interatomic potential][BCC vanadium]
- https://arxiv.org/abs/2208.14420[Intercalation chemistry][Disordered rockSalt Li3V2O5 anode][Cluster expansion][Machine learning interatomic potential]
- https://arxiv.org/abs/2208.10082[Machine learning interatomic potential][Anisotropic thermal transport][Bulk hexagonal boron nitride]
- https://arxiv.org/abs/2205.01209[Machine learning interatomic potential][Simulation][Carbon][Extreme condition]
- https://arxiv.org/abs/2112.14533[Hessian-based assessment][Atomic force][Training machine learning interatomic potential]
- https://arxiv.org/abs/2309.16239[Beam induced heating][Electron microscopy][Machine learning interatomic potential]
- https://arxiv.org/abs/2312.11708[Accelerating][Prediction][Inorganic surface][Machine learning interatomic potential]
- https://arxiv.org/abs/2401.02284[Enhancing][Quality][Reliability][Machine learning interatomic potential][Better reporting practice]
- https://arxiv.org/abs/2402.05222[Validation workflow][Machine learning interatomic potential][Complex ceramics]
- https://arxiv.org/abs/2402.07472[Cartesian atomic cluster expansion][Machine learning interatomic potential]
- https://arxiv.org/abs/2402.18891[Benchmarking phonon anharmonicity][Machine learning interatomic potential]
- https://arxiv.org/abs/2403.01980[Chemical transferability][Accuracy][Ionic liquid simulation][Machine learning interatomic potential]
- https://arxiv.org/abs/2403.10154[Structure-property relation][Silicon oxycarbide][Machine learning interatomic potential]
- https://arxiv.org/abs/2403.18122[Adaptive loss weighting][Machine learning interatomic potential]
- https://arxiv.org/abs/2404.10746[Interpolation and differentiation][Alchemical degrees of freedom][Machine learning interatomic potential]
- https://arxiv.org/abs/2404.18393[Machine learning interatomic potential][Keras API]
- https://arxiv.org/abs/2405.14960[Ni-Cr complexation][FLiBe molten salt][Machine learning interatomic potential][J. Mol. Liq. 400, 124521 (2024)]
- https://arxiv.org/abs/2406.10915[Self-consistent Coulomb interaction][Machine learning interatomic potential]
- https://arxiv.org/abs/2407.00133[Calculation of crystal defects][CaWO4][100 eV displacement cascade][Linear machine learning interatomic potential]
- https://arxiv.org/abs/2407.15404[Accurate estimation][Interfacial thermal conductance][Silicon and diamond][Machine learning interatomic potential]
- https://arxiv.org/abs/2409.07947[Data-efficient multi-fidelity training][High-fidelity machine learning interatomic potential]
- https://arxiv.org/abs/2409.08194[Unifying][Description][Hydrocarbon][Hydrogenated carbon material][Chemically reactive machine learning interatomic potential]
- https://arxiv.org/abs/2409.17869[Best practice][Fitting machine learning interatomic potential][Molten salt][NaCl-MgCl2]
- https://arxiv.org/abs/2112.14308[Reconstructing][Exit wave][High-resolution transmission electron microscopy][Machine learning]
- https://arxiv.org/abs/2201.00629[Low-cost sensor][Indoor PV energy harvesting estimation][Machine learning]
- https://arxiv.org/abs/2201.01629[Topological characterization][Dynamic chiral magnetic texture][Machine learning]
- https://arxiv.org/abs/2201.04949[Nanoscale soil-water retention curve][Unsaturated clay][MD][Machine learning]
- https://arxiv.org/abs/2201.04970[Insight][Cation ordering][Double perovskites oxide][Machine learning]
- https://arxiv.org/abs/2201.04933[Machine learning-enhanced][Efficient spectroscopic ellipsometry modeling]
- https://arxiv.org/abs/2407.15573[Machine learning-enhanced design][Lead-free halide perovskite material][Density functional theory]
- https://arxiv.org/abs/2201.05683[Machine learning technique][Construct detailed phase diagram][Skyrmion system]
- https://arxiv.org/abs/2201.05849[Machine learning][Predict][L-point direct bandgap][Bi1-xSbx nanomaterial]
- https://arxiv.org/abs/2201.08091[Predicting][Machine learning][Structural instabilities][2D material]
- https://arxiv.org/abs/2201.07889[Machine learning enhances algorithm][Quantifying non-equilibrium dynamics][Correlation spectroscopy experiment][Frame-rate-limited time resolution]
- https://arxiv.org/abs/2201.11662[MeltpoolNet][Melt pool characteristic prediction][Metal additive manufacturing][Machine learning]
- https://arxiv.org/abs/2201.11671[Capture agent free biosensing][Porous silicon array][Machine learning]
- https://arxiv.org/abs/2201.12630[Machine learning study][Magnetic ordering][2D material]
- https://arxiv.org/abs/2202.00380[Machine-learning-enhanced quantum sensor][Accurate magnetic field imaging]
- httpsTopogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials://arxiv.org/abs/2202.01042[Machine learning][Exploring small polaron configurational space]
- https://arxiv.org/abs/2401.12042[Machine learning based prediction][Polaron-vacancy pattern][TiO2(110) surface]
- https://arxiv.org/abs/2409.16179[Machine learning small polaron dynamics]
- https://arxiv.org/abs/2202.01051[Element selection][Functional materials discovery][Integrated machine learning][Atomic contributions]
- https://arxiv.org/abs/1803.03073[Compositional optimization][Hard-magnetic phase][Machine-learning model]
- https://arxiv.org/abs/2007.03839[Machine-learning model][Raman spectra analysis][Twisted bilayer graphene]
- https://arxiv.org/abs/2104.10443[Interpretability][Machine-learning model][Physical science]
- https://arxiv.org/abs/2108.13171[Functional nanomaterials Design][Workflow][Building machine-learning model]
- https://arxiv.org/abs/2109.03751[Scale-invariant machine-learning model][Accelerate][Discovery][Quaternary chalcogenide][Ultralow lattice thermal conductivity]
- https://arxiv.org/abs/2112.06551[Accurate computational prediction][Core-electron binding energies][Carbon-based material][Machine-learning model combining DFT and GW]
- https://arxiv.org/abs/2208.06139[Beyond potential][Integrated machine-learning model]
- https://arxiv.org/abs/2202.01449[Predicting tensorial molecular properties][Equivariant machine-learning model]
- https://arxiv.org/abs/2407.01068[Adaptive energy reference][Machine-learning model][Electronic density of states]
- https://arxiv.org/abs/2202.01885[Machine-learning convex][Texture-dependent macroscopic yield][Crystal plasticity simulation]
- https://arxiv.org/abs/2202.04137[Machine learning][Heterogeneous porous material]
- https://arxiv.org/abs/2202.05255[Topogivity][Machine-learned chemical rule][Discovering topological materials]
- https://arxiv.org/abs/2202.06199[Panoramic mapping][Phonon transport][Ultrafast electron diffraction][Machine learning]
- https://arxiv.org/abs/2202.06763[Machine learning-aided discovery][Superionic solid-state electrolyte][Li-Ion Batteries]
- https://arxiv.org/abs/2402.16190[Accurate prediction][Keyhole depth][Machine learning-aided simulation]
- https://arxiv.org/abs/2202.07372[Towards machine learning][Microscopic mechanism][Formula search][Crystal structure stability]
- https://arxiv.org/abs/2202.09186[Electronic structure machine learning][Surrogate][without training]
- https://arxiv.org/abs/2202.10125[ABO3 perovskites' formability prediction][Crystal structure classification][Machine learning]
- https://arxiv.org/abs/2410.10023[Physics-informed AI][ML-based sparse system identification algorithm][Discovery of PDE's][Representing nonlinear dynamic system]
- https://arxiv.org/abs/2112.07625[Physics-informed machine learning][Optical mode][Composite]
- https://arxiv.org/abs/2202.10423[Physics-informed machine learning][Uncertainty quantification][Mechanics][Heterogeneous material]
- https://arxiv.org/abs/2203.00484[Experimental identification][Second-order non-Hermitian skin effect][Physics-graph-informed machine learning]
- https://arxiv.org/abs/2212.00667[Physically informed machine-learning algorithm][Identification][Two-dimensional atomic crystal]
- https://arxiv.org/abs/2301.09725[Physics-informed machine learning][Asymptotic homogenization][Elliptic equation]
- https://arxiv.org/abs/2312.15301[Inverting][Kohn-Sham equation][Physics-informed machine learning]
- https://arxiv.org/abs/2402.11126[Kolmogorov n-Widths][Multitask physics-informed machine learning][PIML][[Towards robust metrics]
- https://arxiv.org/abs/2403.10682[Evaluation][GlassNet][Physics-informed machine learning][Glass stability][Glass-forming ability]
- https://arxiv.org/abs/2403.14015[Data-driven modeling][Dislocation mobility][Atomistics][Physics-informed machine learning]
- https://arxiv.org/abs/2410.13228[From PINNs to PIKANs][Recent advance][Physics-informed machine learning]
- https://arxiv.org/abs/2202.10715[Extraction][Interaction parameter][α−RuCl3][Neutron data][Machine learning]
- https://arxiv.org/abs/2202.13087[Predicting][Formation][Stability][Oxide perovskite][Extracting underlying mechanism][Machine learning]
- https://arxiv.org/abs/2202.13753[Machine learning-enabled][High-entropy alloy][Discovery]
- https://arxiv.org/abs/2203.02391[Machine-learning][Thermodynamic model][Design][New rare-earth compound]
- https://arxiv.org/abs/2203.03366[Improvement][Gradient descent method][Quantum tensor network machine learning]
- https://arxiv.org/abs/2203.03392[Naturally-meaningful][Efficient descriptor][Machine learning][Material properties][Robust one-shot ab initio descriptor]
- https://arxiv.org/abs/2203.09613[Entangling solid solutions][Machine learning][Tensor network][Materials property prediction]
- https://arxiv.org/abs/2203.10349[Machine learning][Impurity charge-state transition level][Semiconductor][Elemental properties][Multi-fidelity dataset]
- https://arxiv.org/abs/2203.12443[Machine learning aided atomic structure identification][Interfacial ionic hydrate][Atomic force microscopy image]
- https://arxiv.org/abs/2203.14398[Investigation][Bi-particle state][Gate-array-controlled quantum-dot system][Machine learning]
- https://arxiv.org/abs/2204.08151[Machine-learning rationalization][Prediction][Solid-state synthesis condition]
- https://arxiv.org/abs/2204.09820[Machine learning][Optical scanning probe nanoscopy]
- https://arxiv.org/abs/2204.11996[Understanding creep suppression mechanism][Polymer nanocomposite][Machine learning]
- https://arxiv.org/abs/2205.01591[Machine learning][Density functional theory]
- https://arxiv.org/abs/2205.02121[Accelerating phase-field-based simulation][Machine learning]
- https://arxiv.org/abs/2205.02967[Putting density functional theory][Test][Machine-learning-accelerated materials discovery]
- https://arxiv.org/abs/2205.04547[Machine learning][Diffusion Monte Carlo energy densities]
- https://arxiv.org/abs/2205.04732[Machine-learned spin-lattice potential][Dynamic simulation][Defective magnetic iron]
- https://arxiv.org/abs/2207.09423[Exploring][Configurational space][Amorphous graphene][Machine-learned atomic energies]
- https://arxiv.org/abs/2205.10046[GPUMD][Package][Constructing accurate machine-learned potential][Performing highly efficient atomistic simulation]
- https://arxiv.org/abs/2209.12948[Developing machine-learned potential][Coarse-grained molecular simulation][Challenges and pitfalls]
- https://arxiv.org/abs/2309.08937[Machine-learned potential energy surface][Free sodium cluster][Density functional accuracy][Applications to melting]
- https://arxiv.org/abs/2310.05279[Combining][D3 dispersion correction][Neuroevolution machine-learned potential]
- https://arxiv.org/abs/2311.01664[Theoretical case study][Generalisation][Machine-learned potential]
- https://arxiv.org/abs/2311.04732[General-purpose machine-learned potential][16 elemental metals][Their alloys]
- https://arxiv.org/abs/2401.16249[Molecular dynamics simulation][Heat transport][Machine-learned potential][Mini review][Tutorial][GPUMD][Neuroevolution potential]
- https://arxiv.org/abs/2404.02626[Polyvalent machine-learned potential][Cobalt][From bulk to nanoparticles]
- https://arxiv.org/abs/2407.15175[Asparagus][Toolkit][Autonomous][User-guided construction][Machine-learned potential energy surface]
- https://arxiv.org/abs/2408.03058[Dual-cutoff machine-learned potential][Condensed organic system][Uncertainty-guided active learning]
- https://arxiv.org/abs/2409.07610[More data hurts][Optimizing data coverage][Mitigating diversity][Induced underfitting][Ultra-fast machine-learned potential]
- https://arxiv.org/abs/2409.16039[Machine-learning potential][Phonon transport][AlN with defects][Multiple charge state]
- https://arxiv.org/abs/2407.03448[Accurate nuclear quantum statistics][Machine-learned classical effective potential]
- https://arxiv.org/abs/2106.03369[Modeling refractory high-entropy alloy][Efficient machine-learned interatomic potential][Defect][Segregation]
- https://arxiv.org/abs/1906.07816[Machine-learned interatomic potential][Alloy][Alloy Phase Diagram]
- https://arxiv.org/abs/2109.15002[Thermal transport][Phase transition][Zirconia][On-the-fly machine-learned interatomic potential]
- https://arxiv.org/abs/2104.05853[Large scale structure prediction][Near-stoichiometric magnesium oxide][Machine-learned interatomic potential][Novel crystalline phase][Oxygen-vacancy ordering]
- https://arxiv.org/abs/2203.08458[Simple machine-learned interatomic potential][Complex alloy]
- https://arxiv.org/abs/2211.12484[How to validate][Machine-learned interatomic potential]
- https://arxiv.org/abs/2303.02519[Validation][Machine-learned interatomic potential][Temperature-dependent electron thermal diffuse scattering]
- https://arxiv.org/abs/2304.01650[Constructing][Evaluating][Machine-learned interatomic potential][Li-based disordered rocksalt]
- https://arxiv.org/abs/2304.03109[Unraveling][Crystallization kinetics][Ge2Sb2Te5 phase change compound][Machine-learned interatomic potential]
- https://arxiv.org/abs/2403.15897[Genetic algorithm][Trained machine-learned interatomic potential][Silicon-carbon system]
- https://arxiv.org/abs/2404.15128[Unveiling][Crystallization kinetics][Ge-rich GexTe alloys][Large scale simulation][Machine-learned interatomic potential]
- https://arxiv.org/abs/2406.19462[Strong atomic reconstruction][Twisted bilayer][Highly flexible InSe][Machine-learned interatomic potential][Continuum model approaches]
- https://arxiv.org/abs/2408.15779[Fast and accurate machine-learned interatomic potential][Large-scale simulation][Cu, Al and Ni]
- https://arxiv.org/abs/2409.11808[Accelerating][Training and improving][Reliability][Machine-learned interatomic potential][Strongly anharmonic material][Active learning]
- https://arxiv.org/abs/2305.02158[Shotgun crystal structure prediction][Machine-learned formation energies]
- https://arxiv.org/abs/2306.15153[Experimental exploration][ErB2][SHAP analysis][Machine-learned model][Magnetocaloric material][Materials design]
- https://arxiv.org/abs/2401.00072[Machine-learned model][Magnetic material]
- https://arxiv.org/abs/2405.01240[Machine-learned tuning][Artificial Kitaev chain][Tunneling-spectroscopy measurement]
- https://arxiv.org/abs/2407.11450[Machine-learned kinetic energy model][Light weight metals and compounds][Group III-V element]
- https://arxiv.org/abs/2408.12390[Density dependence][Thermal conductivity][Nanoporous and amorphous carbon][Machine-learned molecular dynamics]
- https://arxiv.org/abs/2410.07886[Homogeneous nucleation][Undercooled Al-Ni melt][Machine-learned interaction potential]
- https://arxiv.org/abs/2205.05729[Nonlocal machine learning][Micro-structural defect evolution][Crystalline material]
- https://arxiv.org/abs/2205.07925[Machine learning][Relativity-inspired quantum dynamics]
- https://arxiv.org/abs/2008.08793[Explainable machine learning][Materials discovery][Predicting][Potentially formable][Nd-Fe-B crystal structure][Extracting structure-stability relationship]
- https://arxiv.org/abs/2205.10084[Spinel nitride solid solution][Charting properties][Configurational space][Explainable machine learning]
- https://arxiv.org/abs/2303.03748[Computing formation enthalpies][Explainable machine learning method][Lanthanide orthophosphate][Solid solution]
- https://arxiv.org/abs/2308.07823[Explainable machine learning][Hydrogen diffusion][Metal][Random binary alloy]
- https://arxiv.org/abs/2406.04445[Explainable machine learning identification][Superconductivity][Single-particle spectral function]
- https://arxiv.org/abs/2205.13631[Size-dependent nucleation][Crystal phase transition][Machine learning metadynamics][Phys. Rev. Lett. 129, 185701 (2022)]
- Ling Liu, Yingqi Tian, Xuanye Yang, and Chungen Liu, Phys. Rev. Lett. 131, 158001 (2023)[Mechanistic insight][Water autoionization][Metadynamics simulation][Enhanced][Machine learning].
- https://arxiv.org/abs/2206.01669[Relating plasticity][Dislocation][Data analysis][Scaling vs. machine learning]
- https://arxiv.org/abs/2206.04968[Information][Necessary][Sufficient][Predict materials properties][Machine learning]
- https://arxiv.org/abs/2206.06031[Universal synthetic dataset][Machine learning][Spectroscopic data]
- https://arxiv.org/abs/2206.07605[Quantum-corrected thickness-dependent thermal conductivity][Amorphous silicon][Predicted][Machine-learning molecular dynamics simulation]
- https://arxiv.org/abs/2206.08296[Finite temperature dielectric properties][KTaO3][Machine learning][Phonon spectra][Barrett law][Strain engineering][Electrostriction]
- https://arxiv.org/abs/2206.08048[Automated analysis][Continuum field][Atomistic simulation][Statistical machine learning]
- https://arxiv.org/abs/2206.08951[Denoising scanning tunneling microscopy image][Machine learning]
- https://arxiv.org/abs/2206.12239[Machine learning][Edge energies][High symmetry Au nanoparticle]
- https://arxiv.org/abs/2206.15370[Development][Exchange-correlation functional][Machine learning]
- https://arxiv.org/abs/2206.15075[Topological data analysis][Machine learning]
- https://arxiv.org/abs/2207.03599[Quantum chemical root][Machine-learning molecular similarity descriptor]
- https://arxiv.org/abs/2207.04994[Uncertainty-aware][Mixed-variable machine learning][Materials design]
- https://arxiv.org/abs/2011.08426[First-principles prediction][Electronic transport][Experimental semiconductor heterostructure][Physics-based machine learning]
- https://arxiv.org/abs/2207.05171[Physics-based machine-learning approach][Modeling][Temperature-dependent yield strength][Medium- or high-entropy alloy]
- https://arxiv.org/abs/2311.11364[Local environment-based machine learning][Molecular adsorption energy prediction]
- https://arxiv.org/abs/2207.06031[Unsupervised recognition][Informative feature][Tensor network machine learning][Quantum entanglement variation]
- https://arxiv.org/abs/2409.12462[Unsupervised reward-driven image segmentation][Automated scanning transmission electron microscopy experiment]
- https://arxiv.org/abs/2207.10144[Statistical mechanics][Material][First principles calculation][Machine learning]
- https://arxiv.org/abs/2207.12118[Machine-learning][Accelerated identification][Exfoliable two-dimensional material]
- https://arxiv.org/abs/2207.12837[Classical and quantum machine learning application][Spintronics]
- https://arxiv.org/abs/2207.14622[Innate dynamics][Identity crisis][Metal surface][Unveiled][Machine learning][Atomic environments]
- https://arxiv.org/abs/2208.02182[Machine learning optimization][Majorana hybrid nanowire][Phys. Rev. Lett. 130, 116202 (2023)]
- https://arxiv.org/abs/2208.04146[Linking properties][Microstructure][Liquid metal embedded elastomers][Machine learning]
- https://arxiv.org/abs/2208.06923[Predicting creep failure][Machine learning][Which features matter?]
- https://arxiv.org/abs/2208.05546[Data-driven machine learning][Predict mechanical properties][Monolayer TMD]
- https://arxiv.org/abs/2208.04976[Machine learning][1- and 2-electron reduced density matrices][Polymeric molecule]
- https://arxiv.org/abs/1909.08565[Construction][Machine learned force field][Quantum chemical accuracy][Application][Chemical insight]
- https://arxiv.org/abs/2206.00540[Piezo- and pyroelectricity][Zirconia][Machine learned force field]
- https://arxiv.org/abs/2407.15290[Shock hugoniot calculation][On-the-fly machine learned force field][Ab initio accuracy]
- https://arxiv.org/abs/2408.07554[Cyclic and helical symmetry-informed machine learned force field][Application][Lattice vibration][Carbon nanotube]
- https://arxiv.org/abs/2409.16051[Tracing ion migration][Halide provskites][Machine learned force field]
- https://arxiv.org/abs/2308.01278[Machine learned force-field][Ab-initio quality description][Metal-organic framework]
- https://arxiv.org/abs/2009.04045[Experimentally driven][Automated machine learned][Inter-Atomic Potential][Refractory oxide]
- https://arxiv.org/abs/2101.00049[Particle swarm][Hyper-parameter optimization][Machine learned interatomic potential]
- https://arxiv.org/abs/2203.16055[Benchmarking][Structural evolution method][Training of machine learned iInteratomic potential]
- https://arxiv.org/abs/2208.09692[Carbon phase diagram][Empirical][Machine learned interatomic potential]
- https://arxiv.org/abs/1910.12052[Classification][Quantification][Crystal defect][Energetic bombardment][Machine learned molecular dynamics simulation]
- https://arxiv.org/abs/2207.11828[Optimal data generation][Machine learned interatomic potential]
- https://arxiv.org/abs/2309.08689[Collinear-spin machine learned interatomic potential][Fe7Cr2Ni alloy]
- https://arxiv.org/abs/2409.06242[Investigating ionic diffusivity][Amorphous solid electrolytes][Machine learned interatomic potential]
- https://arxiv.org/abs/2409.07039[Combining Brillouin spectroscopy][Machine learned interatomic potential][Probe mechanical properties][Metal organic framework]
- https://arxiv.org/abs/2010.01976[Computational study][Crystal defects formation][Machine learned molecular dynamics simulation]
- https://arxiv.org/abs/2404.00050[Grappa][Machine learned molecular mechanics force field]
- https://arxiv.org/abs/2305.02990[Accelerating GW calculation][Machine learned dielectric matrices]
- https://arxiv.org/abs/2306.00970[Improving][Reliability][Machine learned potential][Modeling inhomogenous liquid]
- https://arxiv.org/abs/2306.02255[Exploring model complexity][Machine learned potential][Simulated properties]
- https://arxiv.org/abs/2309.03840[Generating minimal training set][Machine learned potential][Phys. Rev. Lett. 132, 167301 (2024)]
- https://arxiv.org/abs/2403.00377[Emergence][Accurate atomic energies][Machine learned noble gas potential]
- https://arxiv.org/abs/2407.09628[Accelerating electron dynamics simulation][Machine learned time propagator]
- https://arxiv.org/abs/2208.11448[nanoNET][Machine learning platform][Predicting nanoparticles distribution][Polymer matrix]
- https://arxiv.org/abs/2005.05618[Machine learning guided discovery][Gigantic magnetocaloric effect][HoB2][Near hydrogen liquefaction temperature]
- https://arxiv.org/abs/2207.05546[Enhancing thermoelectric properties][Isotope graphene nanoribbon][Machine learning guided manipulation][Disordered antidot][Interface]
- https://arxiv.org/abs/2208.13742[Machine learning guided high-throughput search][Non-oxide garnet]
- https://arxiv.org/abs/2303.09342[Machine learning guided discovery][Superconducting calcium borocarbide]
- https://arxiv.org/abs/2406.12850[Machine learning guided discovery][Stable][Spin-resolved topological insulator]
- https://arxiv.org/abs/2209.00628[Monotonic Gaussian process][Physics-constrained machine learning][Materials science application]
- https://arxiv.org/abs/2209.03173[Machine learning][High-entropy alloy][Progress][Challenge][Opportunities]
- https://arxiv.org/abs/2209.04891[Statistical perspective][Predicting][Strength of metals][Revisiting][Hall-Petch relationship][Machine learning]
- https://arxiv.org/abs/2202.00930[Machine learning based prediction][Electronic structure][Quasi-one-dimensional material][Under strain]
- https://arxiv.org/abs/2209.01358[Machine learning based approach][Solving atomic structure][Nanomaterials combining pair distribution function][Density functional theory]
- https://arxiv.org/abs/2209.07145[Machine learning based modeling][Disordered elemental semiconductor][Understanding][Atomic structure][a-Si][a-C]
- https://arxiv.org/abs/2301.07790[Machine learning based approach][Predict ductile damage model parameter][Polycrystalline metal]
- https://arxiv.org/abs/2407.09514[Machine learning based prediction][Proton conductivity][Metal-organic framework]
- https://arxiv.org/abs/2407.15338[Revealing][Molecular structure][a-Al2O3(0001)-water interface][Machine learning based computational vibrational spectroscopy]
- https://arxiv.org/abs/2208.05672[Searching][Chromate replacement][Natural language processing][Machine learning algorithm]
- https://arxiv.org/abs/2209.07578[Pixel-wise classification][Graphene-detection][Tree-based machine learning algorithm]
- https://arxiv.org/abs/2409.14370[Machine learning algorithm][Optimization][Magnetocaloric effect][All-d-metal Heusler alloy]
- https://arxiv.org/abs/2209.12605[Mechanical properties prediction][Metal additive manufacturing][Machine learning]
- https://arxiv.org/abs/2209.13949[Application][Machine learning][Mechanical properties][Copper graphene composite]
- https://arxiv.org/abs/2210.02410[Vendi score][Diversity evaluation metric][Machine learning]
- https://arxiv.org/abs/2210.03066[MagNet][Machine learning][Enhanced three-dimensional magnetic reconstruction]
- https://arxiv.org/abs/2210.02666[When not to use machine learning][Perspective][Potential][Limitation]
- https://arxiv.org/abs/2210.07930[Machine learning frontier orbital energies][Nanodiamond]
- https://arxiv.org/abs/2210.09783[Machine-learning-optimized][Perovskite nanoplatelet synthesis]
- https://arxiv.org/abs/2210.10391[Machine learning][Sustainable energy future]
- https://arxiv.org/abs/2210.10700[Basic electro-topological descriptor][Prediction][Organic molecule geometries][Simple machine learning]
- https://arxiv.org/abs/2210.12132[Perspective][Machine learning][Data science][Strongly correlated electron problem]
- https://arxiv.org/abs/2405.02618[Combining machine learning model][First-principles high-throughput calculation][Accelerate][Search][Promising Thermoelectric Materials]
- https://arxiv.org/abs/1709.02727[Machine learning modeling][Superconducting critical temperature]
- https://arxiv.org/abs/1806.01038[Predicting][Thermodynamic stability][Perovskite oxide][Machine learning model]
- https://arxiv.org/abs/1906.02889[Machine learning modeling][High entropy alloy][Short-range order]
- https://arxiv.org/abs/1906.06378[Machine learning model][Lattice thermal conductivity prediction][Inorganic Material]
- https://arxiv.org/abs/2006.06252[Machine learning model][Cluster][Map tribocorrosion regime][Feature space]
- https://arxiv.org/abs/2010.13306[Database][Machine learning Model][Identify thermally driven][Metal-insulator transition compound]
- https://arxiv.org/abs/2102.00191[Importance][Feature engineering][Database selection][Machine learning model][Case study][Carbon crystal structure]
- https://arxiv.org/abs/2107.07997[Uncertainty prediction][Machine learning model]
- https://arxiv.org/abs/2109.08098[MOFSimplify][Machine learning model][Extracted stability data][Three thousand metal-organic frameworks]
- https://arxiv.org/abs/2201.00798[Descriptor][Machine learning model][Generalized force field][Condensed matter system]
- https://arxiv.org/abs/2203.01276[Machine learning model][Predict calculation outcome][Transferability necessary][Computational catalysis]
- https://arxiv.org/abs/2203.01376[Homogeneous ice nucleation][Ab initio machine learning model][Water]
- https://arxiv.org/abs/1908.02398[Assessing and improving machine learning model][Prediction][Polymer glass transition Temperature]
- https://arxiv.org/abs/1908.00926[Accelerating approach][Designing ferromagnetic material][Machine leaning modeling][Magnetic ground state][Curie temperature]
- https://arxiv.org/abs/2205.03708[Description][Collective magnetization process][Machine learning model]
- https://arxiv.org/abs/2210.08878[Hundreds][New][Stable][One-dimensional material][Generative machine learning model]
- https://arxiv.org/abs/2210.13587[Quantifying][Performance][Machine learning model][Materials discovery]
- https://arxiv.org/abs/2210.14191[Database][Ultrastable MOFs][Reassembled][Stable fragment][Machine learning model]
- https://arxiv.org/abs/2407.01485[Machine learning model][Atom-diatom reactions across isotopologues]
- https://arxiv.org/abs/2407.08390[Chemical bond-based machine learning model][Dipole moment][Application][Dielectric properties][Liquid methanol and ethanol]
- https://arxiv.org/abs/2407.19241[Advancement][Tuning thermoelectric properties][Insight][Hybrid functional studies][Strain engineering][Machine learning model]
- https://arxiv.org/abs/2409.06921[Voltage mining][(De)lithiation-stabilized Cathode][Machine learning model][Li-ion cathode voltage]
- https://arxiv.org/abs/2409.12284[Best of both worlds][Enforcing detailed balance][Machine learning model][Transition rate]
- https://arxiv.org/abs/2410.09659[Many-body expansion][Machine learning model][Octahedral transition metal complexes]
- https://arxiv.org/abs/2210.15070[Noise robust automatic charge state recognition][Quantum dot][Machine learning][Pre-processing][Visual explanation][Grad-CAM]
- https://arxiv.org/abs/2211.00691[Accelerated design][Chalcogenide Glasses][Interpretable machine learning][Composition property relationship]
- https://arxiv.org/abs/2211.01624[Accelerating][Discovery][g-C3N4-supported single atom catalysts][Hydrogen evolution reaction][Combined DFT and machine learning strategy]
- https://arxiv.org/abs/2211.01583[Data-based polymer-unit fingerprint (PUFp)][Newly accessible expression][Polymer organic semiconductor][Machine learning]
- https://arxiv.org/abs/2211.03075[Prediction][Superconducting properties][Machine learning model]
- https://arxiv.org/abs/2211.03265[Machine-learning approach][Discovery][Conventional superconductor]
- https://arxiv.org/abs/2211.03223[Cementron][Machine learning][Constituent phase][Cement clinker][Optical image]
- https://arxiv.org/abs/2211.03500[Structure][Solidification][(Fe0.75B0.15Si0.1)100-xTax (x=0-2) melts][Experiment][Machine learning]
- https://arxiv.org/abs/2211.04504[All rf-based tuning algorithm][Quantum device][Machine learning]
- https://arxiv.org/abs/2211.06490[Non-volatile][All-spin non-binary matrix multiplier][Efficient hardware accelerator][Machine learning]
- https://arxiv.org/abs/2211.08194[Machine learning][Interpreting coherent X-ray speckle pattern]
- https://arxiv.org/abs/2211.09239[Machine learning][Nuclear materials research]
- https://arxiv.org/abs/2211.15415[Machine learning][Screening large organic molecule]
- https://arxiv.org/abs/2211.16486[AdsorbML][Accelerating adsorption energy calculation][Machine learning]
- https://arxiv.org/abs/2212.00634[Discrete element simulation][Machine learning][Improving][Performance][Dry catalyst continuous impregnation process]
- https://arxiv.org/abs/2212.01432[Machine learned interatomic potential][Dispersion strengthened plasma facing component]
- https://arxiv.org/abs/2212.04322[Encrypted machine learning][Molecular quantum properties]
- https://arxiv.org/abs/2212.05213[Fusing machine learning strategy][Density functional theory][Hasten][Discovery][MXene][Hydrogen generation]
- https://arxiv.org/abs/2212.05175[Machine learning reconstruction][Depth-dependent thermal conductivity profile][Frequency-domain thermoreflectance signal]
- https://arxiv.org/abs/2212.06755[Machine learning superalloy microchemistry][Creep strength][Physical descriptor]
- https://arxiv.org/abs/2212.06929[Generating extreme quantum scattering][Graphene][Machine learning]
- https://arxiv.org/abs/2405.09057[Response matching][Generating materials and molecules]
- https://arxiv.org/abs/2212.10478[Machine learning][Polymer self-consistent field theory][Two spatial dimension]
- https://arxiv.org/abs/2212.10283[Interpretable model][Extrapolation][Scientific machine learning]
- https://arxiv.org/abs/2212.11855[Closed-loop machine learning][Discovery][Novel superconductor]
- https://arxiv.org/abs/2301.01959[Application][Machine learning][Sporadic experimental data][Understanding epitaxial strain relaxation]
- https://arxiv.org/abs/2301.03030[Exploring high thermal conductivity polymer][Interpretable machine learning][Physical descriptor]
- https://arxiv.org/abs/2301.03372[Machine-Learning prediction][Computed band gap][Double perovskite materials]
- https://arxiv.org/abs/2301.07906[Machine-learning][Spectral function][Hole][Quantum antiferromagnet]
- https://arxiv.org/abs/2303.16597[Machine-learning surrogate model][Accelerating][Search][Stable ternary alloy]
- https://arxiv.org/abs/2304.05905[Machine-learning recognition][Dzyaloshinskii-Moriya Interaction][Magnetometry]
- https://arxiv.org/abs/2304.08871[Machine-learning detection][Berezinskii-Kosterlitz-Thouless transition][Second-order phase transition][XXZ model]
- https://arxiv.org/abs/2305.07251[Machine-learning-accelerated simulation][Heuristic-free surface reconstruction]
- https://arxiv.org/abs/2312.03598[Machine-learning-accelerated][Quantum transport study][Effects of superlattice disorder][Strain][Mid-wave infrared curved sensor]
- https://arxiv.org/abs/1911.03307[Pushing the limits of atomistic simulations][Ultra-high temperature][Machine-learning force field][ZrB2]
- https://arxiv.org/abs/1912.10761[Complexity][Transferability][Machine-learning force field][Gold-iron interaction]
- https://arxiv.org/abs/2110.00321[Exploring librational pathway][On-the-fly machine-learning force field][Methylammonium molecule][MAPbX3 (X=I, Br, Cl) perovskite]
- https://arxiv.org/abs/2101.06099[Anharmonic lattice dynamics][Large thermodynamic ensemble][Machine-learning force field][Breakdown][Phonon quasiparticle picture][CsPbBr3]
- https://arxiv.org/abs/2303.16538[Efficient generation][Stable linear machine-learning force field][Uncertainty-aware active learning]
- https://arxiv.org/abs/2305.10255[First-principles machine-learning force field][Heterogeneous ice nucleation][Microcline feldspar]
- https://arxiv.org/abs/2407.13507[Strength][2D glasses][Machine-learning force field]
- https://arxiv.org/abs/2305.12060[Mechanical property design][Bio-compatible Mg alloy][Machine-learning algorithm]
- https://arxiv.org/abs/1905.02142[Optimizing many-body atomic descriptor][Enhanced computational performance][Machine-learning-based interatomic potential]
- https://arxiv.org/abs/2008.07120[Machine-learning-based sampling method][Exploring local energy minima][Interstitial species in a crystal]
- https://arxiv.org/abs/2010.12467[Machine-learning-based prediction][Lattice thermal conductivity][Half-Heusler compound][Atomic information]
- https://arxiv.org/abs/2105.08940[Thermo-mechanical][Nitrogenated holey graphene (C2N)][Comparison][Machine-learning-based][Classical interatomic potential]
- https://arxiv.org/abs/2110.02827[Colmena][Scalable machine-learning-based steering][Ensemble simulation][High performance computing]
- https://arxiv.org/abs/2111.15593[Machine-learning-based exchange-correlation functional][Physical asymptotic constraint]
- https://arxiv.org/abs/2112.02587[Machine-learning-based intelligent framework][Discovering refractory high-entropy alloy][Improved high-temperature yield strength]
- https://arxiv.org/abs/2304.13932[Yield strength-plasticity][Trade-off][Uncertainty quantification][Machine-learning-based Design][Refractory high-entropy alloy]
- https://arxiv.org/abs/2310.15591[Machine-learning-based non-local kinetic energy density functional][Simple metals and alloys]
- https://arxiv.org/abs/2306.11899[Closing the loop][Autonomous experiment][Enabled][Machine-learning-based online data analysis][Synchrotron beamline environment]
- https://arxiv.org/abs/2408.12627[Machine-learning-based construction][Molecular potential and its application][Exploring the deep-lying-orbital effect][HighOrder harmonic generation]
- https://arxiv.org/abs/1812.01025[Accelerating photovoltaic materials development][High-throughput Experiment][Machine-learning-assisted diagnosis]
- https://arxiv.org/abs/1908.00739[Machine-learning-assisted thin-film growth][Bayesian optimization][Molecular beam epitaxy][SrRuO3 thin films]
- https://arxiv.org/abs/2210.00579[Large-scale machine-learning-assisted exploration][Whole materials space]
- https://arxiv.org/abs/2306.12898[Machine-learning-assisted][Real-time-feedback-controlled growth][InAs/GaAs quantum dot]
- https://arxiv.org/abs/2308.15907[Machine-learning-assisted construction][Ternary convex hull diagram]
- https://arxiv.org/abs/2310.14205[Machine-learning-assisted analysis][Transition metal dichalcogenide][Thin-film growth]
- https://arxiv.org/abs/2308.11867[Atomic scale understanding][Initial Cu-Ni oxidation][Machine-learning accelerated first-principles simulation][In situ TEM experiment]
- https://arxiv.org/abs/2310.09062[Mechanisms of temperature-dependent thermal transport][Amorphous silica][Machine-learning molecular dynamics]
- https://arxiv.org/abs/2310.15314[Combining linear-scaling quantum transport][Machine-learning molecular dynamics][Thermal and electronic transports][Complex material]
- https://arxiv.org/abs/2311.00633[Ab initio machine-learning unveils strong anharmonicity][Non-Arrhenius self-diffusion][Tungsten]
- https://arxiv.org/abs/2311.01099[Dissimilar thermal transport properties][kappa-Ga2O3][beta-Ga2O3][Machine-learning homogeneous nonequilibrium molecular dynamics simulation]
- https://arxiv.org/abs/2401.12127[Machine-learning structural reconstruction][Accelerated point defect calculation]
- https://arxiv.org/abs/2401.11244[Accurate description][Ion migration][Solid-state ion conductor][Machine-learning molecular dynamics]
- https://arxiv.org/abs/2406.15956[Decoupling many-body interaction][CeO2 (111) oxygen vacancy structure][Insights][Machine-learning][Cluster expansion]
- https://arxiv.org/abs/2311.11305[Machine-larning-based interatomic potential][Group IIB][VIA Semiconductor][Comparative study][Universal and independent model]
- https://arxiv.org/abs/2311.11990[Machine-learned atomic cluster expansion potential][Fast and quantum-accurate thermal simulation][Wurtzite AlN]
- https://arxiv.org/abs/2405.06465[Quantum-accurate machine learning potential][Metal-organic framework][Temperature driven active learning]
- https://arxiv.org/abs/2312.01662[Universal deoxidation][Semiconductor substrate][Machine-learning][Real-time-feedback-control]
- https://arxiv.org/abs/2301.08073[Glass hardness][Predicting composition][Load effect][Symbolic reasoning-informed machine learning]
- https://arxiv.org/abs/2301.07743[CycleGAN][Generate realistic STEM image][Machine learning]
- https://arxiv.org/abs/2301.08813[Representation][Material][Machine learning]
- https://arxiv.org/abs/2301.10474[Machine learning][Structural representation][Discovery][High temperature superconductor]
- https://arxiv.org/abs/2301.12639[Machine learning][Robust prediction][Thermal boundary conductance][2D substrate interface]
- https://arxiv.org/abs/2301.13550[Linear Jacobi-Legendre expansion][Charge density][Machine learning-accelerated electronic structure calculation]
- https://arxiv.org/abs/2311.01491[Investigating][Behavior of diffusion model][Accelerating electronic structure calculation]
- https://arxiv.org/abs/2301.13435[Exciton diffusion][Amorphous organic semiconductor][Reducing simulation overhead][Machine learning]
- https://arxiv.org/abs/2302.03362[Machine learning benchmark][Classification][Equivalent circuit model][Solid-state electrochemical impedance spectra]
- https://arxiv.org/abs/2302.03146[Combining][Synchrotron X-ray diffraction][Mechanistic modeling][Machine Learning][In situ subsurface temperature quantification][Additive manufacturing]
- https://arxiv.org/abs/2302.03321[Machine-learning accelerated annealing][Fitting-search style][Multi-alloy structure prediction]
- https://arxiv.org/abs/2302.09542[Dynamics of growing carbon nanotube interface][Machine learning-enabled molecular simulation]
- https://arxiv.org/abs/2406.16211[Machine-learning-enabled fast optical identification][Characterization][2D material]
- https://arxiv.org/abs/2311.15549[From prediction to action][Critical role][Proper performance estimation][Machine-learning-driven materials discovery]
- https://arxiv.org/abs/1803.02802[Realistic atomistic structure][Amorphous silicon][Machine-learning-driven][Molecular dynamics]
- https://arxiv.org/abs/2006.09760[Machine-learning-driven simulated deposition][Carbon film][Low-density][Diamond-like amorphous carbon]
- https://arxiv.org/abs/2302.09337[Revealing intrinsic vortex-core state][Fe-based superconductor][Machine-learning-driven discovery]
- https://arxiv.org/abs/2401.14039[Threshold displacement energy map][Frenkel pair generation][Ga2O3][Machine-learning-driven molecular dynamics simulation]
- https://arxiv.org/abs/2409.06071[Constructing multicomponent cluster expansion][Machine-learning][Chemical embedding]
- https://arxiv.org/abs/2302.09242[Deciphering alloy composition][Superconducting single-layer FeSe1-xSx][SrTiO3(001) substrate][Machine learning][STM/S data]
- https://arxiv.org/abs/2302.12274[Machine learning microscopic form][Nematic order][Twisted double-bilayer graphene]
- https://arxiv.org/abs/2302.13329[Classification][Magnetic order][Electronic structure][Machine learning]
- https://arxiv.org/abs/2302.13745[Predicting elastic and plastic properties][Small iron polycrystal][Machine learning]
- https://arxiv.org/abs/2303.01886[Machine learning][Magnetic stochastic synapses]
- https://arxiv.org/abs/2303.02247[Machine learning][Twin/matrix interface][Local stress field]
- https://arxiv.org/abs/2303.03493[Machine learning][Phase ordering dynamics][Charge density wave]
- https://arxiv.org/abs/2303.04318[Machine learning model][Capture plasmon dynamics][Ag nanoparticle]
- https://arxiv.org/abs/2303.07441[Linking stability][Molecular geometries][Perovskite][Lanthanide][Richness][Machine learning method]
- https://arxiv.org/abs/2303.07647[Recent advances and applications][Machine learning][Experimental solid mechanics][Review]
- https://arxiv.org/abs/2303.10092[Learning from 2D][Machine learning of 3D effective properties][Heterogeneous material][2D microstructure section]
- https://arxiv.org/abs/2303.09814[Sub-10 nm probing][Ferroelectricity][Heterogeneous material][Machine learning][Contact Kelvin probe force microscopy]
- https://arxiv.org/abs/2303.12486[Machine learning-informed structuro-elastoplasticity][Predict][Ductility][Disordered solid]
- https://arxiv.org/abs/2303.13433[Machine learning-enabled tomographic imaging][Chemical short-range atomic ordering]
- https://arxiv.org/abs/2303.15307[PiNNwall][Heterogeneous electrode model][Integrating machine learning][Atomistic simulation]
- https://arxiv.org/abs/2304.05502[Machine learning][Structure-property relationship][Scalability][Limitation]
- https://arxiv.org/abs/2304.06800[Predicting][Fracture propensity][Amorphous silica][Molecular dynamics simulation][Machine learning]
- https://arxiv.org/abs/2304.07592[Leveraging composition-based material descriptor][Machine learning optimization]
- https://arxiv.org/abs/2303.11244[Machine learning][Phase and size-controlled synthesis][Iron oxides]
- https://arxiv.org/abs/2410.08912[Machine-learning framework][Accelerating spin-lattice relaxation simulation]
- https://arxiv.org/abs/1812.0505[Graph networks][Universal machine learning framework][Molecule][Crystal]
- https://arxiv.org/abs/2401.17587[Semi-supervised machine learning framework][Predicting hydrogen storage capacities][Metal hydride]
- https://arxiv.org/abs/1904.08750[Property-aimed embedding][Machine learning framework][Material discovery]
- https://arxiv.org/abs/2003.12465[Interactive human-machine learning framework][Modelling of ferroelectric-dielectric Composites]
- https://arxiv.org/abs/2101.06474[Optimized and autonomous machine learning framework][Characterizing][Pore][Particle][Grain][Grain boundary][Microstructural image]
- https://arxiv.org/abs/2211.12459[Generalized machine learning framework][Brittle crack problem][Transfer learning][Graph neural network]
- https://arxiv.org/abs/2303.12638[Scientific machine learning framework][Understand flash graphene synthesis]
- https://arxiv.org/abs/2304.08761[Machine learning framework][Quantifying chemical segregation][Microstructural feature][Atom probe tomography data]
- https://arxiv.org/abs/2404.06499[Machine learning framework][Prediction][Grain boundary segregation][Chemically complex environments]
- https://arxiv.org/abs/2304.11497[Machine learning method][Characterize][Crack length][Position][High-density polyethylene][Ultrasound]
- https://arxiv.org/abs/2304.11226[Probabilistic selection][Design][Concrete][Machine learning][Data-Centric Engineering, 4, e9. (2023)]
- https://arxiv.org/abs/2304.12123[Machine learning modeling][Atomic structure][Physical properties][Alkali and alkaline-earth aluminosilicate glasses and melts]
- https://arxiv.org/abs/2304.11828[Machine learning][Predicting fatigue properties][Additively manufactured material]
- https://arxiv.org/abs/2305.00938[Accelerating microstructure modelling][Machine learning][New method][Combining autoencoder and ConvLSTM]
- https://arxiv.org/abs/2305.00229[Accelerated][Inexpensive][Machine learning][Manufacturing processes][Incomplete mechanistic knowledge]
- https://arxiv.org/abs/2305.03666[Machine learning][Accelerated bandgap prediction][Strain-engineered quaternary III-V semiconductor]
- https://arxiv.org/abs/2305.03698[Scratching depth][Ceramic-metal ratio][Scratch behavior][NbC/Nb ceramic/metal nano-laminate][Molecular dynamics simulation][Machine learning]
- https://arxiv.org/abs/2305.02236[High-sensitivity extreme-ultraviolet transient absorption spectroscopy][Enabled][Machine learning]
- https://arxiv.org/abs/2305.06925[Accurate surface and finite temperature bulk properties][Lithium metal][Large scale][Machine learning interaction potential]
- https://arxiv.org/abs/2403.13952[Consideration][ML interaction potential][Free energy calculation]
- https://arxiv.org/abs/2305.08038[First principles][Machine learning][Identify key pairing strength factor][Cuprate superconductor]
- https://arxiv.org/abs/2305.07942[Build a ruler][Measure electron localization function][Machine learning model][S-Au bonds quantification][Thiolate][Protected gold nanocluster]
- https://arxiv.org/abs/2305.11825[Machine learning moment tensor potential][Modelling dislocation][Fracture][L10-TiAl and D019-Ti3Al Alloys]
- https://arxiv.org/abs/2305.12010[Chemellia][Ecosystem][Atomistic scientific machine learning]
- https://arxiv.org/abs/2305.12977[Machine learning][Relationship][Debye and superconducting transition temperatures]
- https://arxiv.org/abs/2305.14764[Detection][Non-uniformity][Parameter][Magnetic domain pattern generation][Machine learning][J. Phys. Soc. Jpn. 93, 054706 (2024)]
- https://arxiv.org/abs/2305.16230[Topological gap protocol][Machine learning optimization][Majorana hybrid wire]
- https://arxiv.org/abs/2305.19440[Machine learning][Tree tensor network][CP rank constraint][Tensor dropout]
- https://arxiv.org/abs/2305.19930[Atom-by-atom design][Metal oxide catalyst][Oxygen evolution reaction][Machine learning]
- https://arxiv.org/abs/2306.01402[Machine learning wave function][Identify fractal phase]
- https://arxiv.org/abs/2306.02015[Machine learning][Experimental design][Parameter estimation][Ultrafast spin dynamics]
- https://arxiv.org/abs/2306.03143[Machine learning feature discovery][Spinon Fermi surface]
- https://arxiv.org/abs/2409.08054[Predicting and accelerating nanomaterials synthesis][Machine learning featurization]
- https://arxiv.org/abs/2306.04426[Machine learning][Universal empirical pseudopotential]
- https://arxiv.org/abs/2306.06032[Machine learning][Electronic structure][Matter across temperature]
- https://arxiv.org/abs/2306.07953[Unraveling magnetic anisotropy energy][Ferromagnetic monolayer][Ferroelectric ABO3][DFT][Machine learning]
- http://arXiv.org/abs/1512.00623[Machine learning-based selective sampling procedure][Identifying the low energy region][Potential energy surface][Proton conduction]
- https://arxiv.org/abs/2102.02470[Machine learning-based generalized model][Finite element analysis][Roll deflection][Austenitic stainless steel 316L][Strip rolling]
- https://arxiv.org/abs/2103.13495[Machine learning-based automatic graphene detection][Color correction][Optical microscope image]
- https://arxiv.org/abs/2104.08103[Accurate prediction][Bonding][Machine learning-based model][Isolated state]
- https://arxiv.org/abs/2201.01932[Machine learning-based classification approach][Phase diagram prediction]
- https://arxiv.org/abs/2201.11188[Crystal structure prediction][Machine learning-based element substitution]
- https://arxiv.org/abs/2209.12946[Investigation][Machine learning-based coarse-grained mapping scheme][Organic molecule]
- https://arxiv.org/abs/2207.00456[Machine learning-based mass density model][Hard magnetic 14:2:1 phase][Chemical composition-based feature]
- https://arxiv.org/abs/2208.02141[Machine learning-based classification][Interpretation][Prediction][High-entropy-alloy][Intermetallic phase]
- https://arxiv.org/abs/2211.00090[Machine learning-based sampling][Virtual experiment][Full stress state][Identify parameters][Anisotropic yield model]
- https://arxiv.org/abs/2306.08387[Machine learning-based prediction][Elastic properties][Amorphous metal alloy][Physica A: Statistical Mechanics and its Applications 617, 128678 (2023)]
- https://arxiv.org/abs/2309.00305[Efficient surrogate model][Materials science simulation][Machine learning-based prediction][Microstructure properties]
- https://arxiv.org/abs/2311.15423[Machine learning-based estimation][Explainable artificial intelligence-supported interpretation][Critical temperature][Magnetic ab initio Heusler alloys data]
- https://arxiv.org/abs/2403.15372[Machine learning-based compression][Quantum many body physics][PCA and autoencoder representation][Vertex function]
- https://arxiv.org/abs/2406.17457[Additively manufacturable high-strength aluminum alloy][Thermally stable microstructure][Hybrid machine learning-based design]
- https://arxiv.org/abs/2408.04055[Machine learning-based reward-driven tuning][Scanning probe microscopy][Fully automated microscopy]
- https://arxiv.org/abs/2408.12181[Machine learning-based determination][Magnetic parameter][Magnetic image][Different imaging scale]
- https://arxiv.org/abs/2306.08595[TensorKrowch][Smooth integration][Tensor network][Machine learning]
- https://arxiv.org/abs/2306.11054[Machine learning renormalization group][Statistical physics]
- https://arxiv.org/abs/2306.10223[Machine learning search][Stable binary Sn alloy][Na, Ca, Cu, Pd, and Ag]
- https://arxiv.org/abs/2306.11978[Designing Pr-based advanced photoluminescent materials][Machine learning][Density functional theory]
- https://arxiv.org/abs/2306.14285[Transferable][Robust][Machine learning model][Predicting stability][Si anode][Multivalent cation batteries]
- https://arxiv.org/abs/2306.14155[Discovering two-dimensional magnetic topological insulator][Machine learning]
- https://arxiv.org/abs/2306.14845[Open-source robust machine learning platform][Real-time detection][Classification][2D material flake]
- https://arxiv.org/abs/2310.07049[Robust machine learning inference][X-ray absorption][Near edge spectra][Featurization]
- https://arxiv.org/abs/2307.03310[Finding][Dynamics][Integrable quantum many-body system][Machine learning]
- https://arxiv.org/abs/2307.04351[MD-HIT][Machine learning][Materials property prediction][Dataset redundancy control]
- https://arxiv.org/abs/2307.05378[M2Hub][Unlocking][Potential][Machine learning][Materials discovery]
- https://arxiv.org/abs/2307.05911[Grain][Grain boundary][Segmentation][Machine learning][Real and generated dataset]
- https://arxiv.org/abs/2307.06209[Structural][Electronic][Thermal][Mchanical properties][C60-based fullerene][Two-dimensional network][First-principles][Machine learning]
- https://arxiv.org/abs/2307.06384[Machine learning accelerated discovery][Corrosion-resistant][High-entropy alloy]
- https://arxiv.org/abs/2407.11208[Machine learning accelerated prediction][Ce-based ternary compound][Antagonistic pair]
- https://arxiv.org/abs/2307.06556[Metal oxide-based gas sensor array][VOCs analysis][Complex Mixtures][Machine learning]
- https://arxiv.org/abs/2307.11068[Machine learning][Majorana nanowire][Disorder landscape][Phys. Rev. Lett. 132, 206602 (2024)]
- https://arxiv.org/abs/2307.10935[Inorganic synthesis-structure map][Zeolite][Machine learning][Crystallographic distance]
- https://arxiv.org/abs/2307.10578[Delta machine learning][Predicting dielectric properties][Raman spectra]
- https://arxiv.org/abs/2210.17484[Open MatSci ML toolkit][Flexible framework][Machine learning][Materials science]
- https://arxiv.org/abs/2310.07864[Towards foundation models][Materials science][Open MatSci ML toolkit]
- https://arxiv.org/abs/2309.05934[MatSciML][Broad][Multi-task benchmark][Solid-state materials modeling]
- https://arxiv.org/abs/2307.14032[Advances of machine learning][Materials science][Ideas and Techniques]
- https://arxiv.org/abs/2307.14627[Preparation][Entangled many-body state][Machine learning]
- https://arxiv.org/abs/2307.15663[CoRe optimizer][All-in-one solution][Machine learning]
- https://arxiv.org/abs/2307.16052[Unveiling exotic magnetic phase][Fibonacci quasicrystalline stacking][Ferromagnetic Layer][Machine learning]
- https://arxiv.org/abs/2308.00665[Machine learning density functional][Random-phase approximation]
- https://arxiv.org/abs/2308.00490[Discovery][Stable hybrid organic-inorganic double perovskites][High-performance solar cell][Machine-learning algorithm][Crystal graph convolution neural network method]
- https://arxiv.org/abs/2308.01443[Evaluation][Optical constant][Oxide thin film][Machine learning]
- https://arxiv.org/abs/2308.01993[Accelerated organic crystal structure prediction][Genetic algorithm][Machine learning]
- https://arxiv.org/abs/2308.01932[Investigation][Machine learning][Estimating][Critical temperature][Superconductor]
- https://arxiv.org/abs/2202.13554[Machine learning method][Material property prediction][Example polymer compatibility]
- https://arxiv.org/abs/2308.02937[Improving realistic material property prediction][Domain adaptation][Machine learning]
- https://arxiv.org/abs/2308.04937[Machine learning][Unveil][Multiple Pauli blockade][Transport spectroscopy][Bilayer graphene double-quantum dot]
- https://arxiv.org/abs/2308.08226[Accelerated design][Block copolymer][Unbiased exploration strategy][Fusion][Molecular dynamics simulation][Machine learning]
- https://arxiv.org/abs/2409.09691[Extrapolative ML Model][Copolymer]
- https://arxiv.org/abs/2308.07649[Primitive machine learning tool][Mechanical property prediction][Multiple principal element alloy]
- https://arxiv.org/abs/2308.14920[Matbench discovery][Evaluation framework][Machine learning crystal stability prediction]
- https://arxiv.org/abs/2308.16868[Understanding defects][Amorphous silicon][Million-atom simulation][Machine learning]
- https://arxiv.org/abs/2309.02362[Prediction][Uncertainty estimate][Reactor pressure vessel steel embrittlement][Machine learning]
- https://arxiv.org/abs/2309.02573[Hydrogen-induced degradation dynamics][Silicon heterojunction solar cell][Machine learning]
- https://arxiv.org/abs/2309.03587[Modelling atomic and nanoscale structure][Silicon-oxygen system][Active machine learning]
- https://arxiv.org/abs/2309.03482[Machine learning][Kinetic energy densities][Target and feature averaging][Better result][Fewer training data]
- https://arxiv.org/abs/2309.04140[How close][Classical two-body potential][Ab initio calculation][Insight][Linear machine learning][Force matching]
- https://arxiv.org/abs/2309.04692[Accelerating discovery][Vacancy ordered 18-valence electron half-Heusler compound][Synergistic approach][Machine learning][Density functional theory]
- https://arxiv.org/abs/2312.17596[Accurate machine learning interatomic potential][FCC and HCP nickel]
- https://arxiv.org/abs/1610.02098[Machine learning force field][Construction][Validation][Outlook]
- https://arxiv.org/abs/1807.02042[Construction][Accurate machine learning force field][Copper and silicon dioxide]
- https://arxiv.org/abs/1904.12961[On-the-fly machine learning force field generation][Application][Melting point]
- https://arxiv.org/abs/2105.02525[Machine learning force field][Local parametrization][Dispersion interaction][Application to the phase diagram of C60]
- https://arxiv.org/abs/2311.11362[Symmetry-invariant][Quantum machine learning force field]
- https://arxiv.org/abs/2403.11705[Coarsening][Chiral domain][Itinerant electron magnet][Machine learning force field approach]
- https://arxiv.org/abs/2403.20138[Na vacancy driven phase transformation][Fast ion conduction][W-doped Na3SbS4][Machine learning force field]
- https://arxiv.org/abs/2406.17595[Density-based long-range electrostatic descriptor][Machine learning force field]
- https://arxiv.org/abs/2405.13635[Machine learning force field][Thermal oxidation of silicon]
- https://arxiv.org/abs/2409.01931[Design space][Molecular mechanics][Machine learning force field]
- https://arxiv.org/abs/2309.05325[Superfolded configuration][Low thermal conductivity][Two-dimensional carbon allotrope][Machine learning force constant potential]
- https://arxiv.org/abs/2311.16964[Machine learning force-field model][Metallic spin glass]
- https://arxiv.org/abs/2309.04478[Multimodal machine learning][Materials science][Composition-structure bimodal learning][Experimentally measured properties]
- https://arxiv.org/abs/2309.06122[Robust synthetic data generation framework][Machine learning][High-resolution transmission electron microscopy][HRTEM]
- https://arxiv.org/abs/2309.15679[Classification][Skyrmionic texture][Extraction][Hamiltonian parameter][Machine learning]
- https://arxiv.org/abs/2309.15127[Grad DFT][Software library][Machine learning][Enhanced density functional theory]
- https://arxiv.org/abs/2309.15868[Mechanical properties][Single and polycrystalline solids][Machine learning]
- https://arxiv.org/abs/2310.03378[Machine learning][Interaction network][Coupled dynamical system]
- https://arxiv.org/abs/2310.03267[Machine learning detection][Correlation][Snapshot][Ultracold atom][Optical lattice]
- https://arxiv.org/abs/2310.04021[Multi-principal element alloy discovery][Directed energy deposition][Machine learning]
- https://arxiv.org/abs/2409.15391[Supply risk-aware][Alloy discovery and design]
- https://arxiv.org/abs/2310.05018[Human-in-the-loop][Machine learning][Automated electron microscopy]
- https://arxiv.org/abs/2310.06598[Kohn-Sham accuracy][Orbital-free density functional theory][Delta-machine learning]
- https://arxiv.org/abs/2310.07089[Machine learning method][Background potential estimation][2DEG]
- https://arxiv.org/abs/2310.07604[Surface segregation][High-entropy alloy][Alchemical machine learning]
- https://arxiv.org/abs/2310.07816[Quantitative analysis][MoS2 thin film micrograph][Machine learning]
- https://arxiv.org/abs/2310.09911[Machine learning many-body Green's function][Molecular excitation spectra]
- https://arxiv.org/abs/2003.04922[Machine-learning assisted cross-domain prediction][Ionic conductivity][Sodium and lithium-based superionic conductors]
- https://arxiv.org/abs/2310.08378[When the atoms dance][Exploring mechanism][Electron-beam][Modification][Machine-learning assisted high temporal resolution electron microscopy]
- https://arxiv.org/abs/2401.05106[Exploring][Formation][Gold/silver nanoalloy][Gas-phase synthesis][Machine-learning assisted simulation]
- https://arxiv.org/abs/2110.13308[Calibrating DFT formation enthalpy calculation][Multi-fidelity machine learning]
- https://arxiv.org/abs/2310.13153[Discovering novel halide perovskite alloy][Multi-fidelity machine learning][Genetic algorithm]
- https://arxiv.org/abs/2310.18439[Machine learning detecting Majorana zero mode][Zero bias peak measurement]
- https://arxiv.org/abs/2311.01401[Machine learning design][Perovskite catalytic properties]
- https://arxiv.org/abs/2311.00787[Accelerating electronic stopping power prediction][10 million times][Combination][Time-dependent density functional theory][Machine learning]
- https://arxiv.org/abs/2311.03139[End-to-end material thermal conductivity prediction][Machine learning]
- https://arxiv.org/abs/2311.02522[Comparison][Different machine learning][Predict viscosity][Tri-n-butyl phosphate mixtures][Experimental data]
- https://arxiv.org/abs/2311.07253[Machine learning][Kondo entanglement cloud][Local measurement]
- https://arxiv.org/abs/2311.07114[Novel model][Fatigue life prediction][Wideband random load][Machine learning]
- https://arxiv.org/abs/2311.09739[Machine learning][Polaritonic chemistry][Accessing chemical kinetics]
- https://arxiv.org/abs/2311.09508[Atoms as Words][Novel approach][Deciphering material properties][NLP-inspired machine learning][Crystallographic information Files (CIFs)]
- https://arxiv.org/abs/2301.03497[Phase transition][Inorganic halide perovskite][Machine learning potential][Journal of Physical Chemistry C 127, 13773 (2023)]
- https://arxiv.org/abs/1901.03346[Machine learning assisted measurement][Local topological invariant]
- https://arxiv.org/abs/1906.11275[Machine learning assisted insight][Spin Ice][Dy2Ti2O7]
- https://arxiv.org/abs/2210.14701[Machine learning assisted design][Optimization][Transition metal-incorporated carbon quantum dot catalyst][Hydrogen evolution reaction]
- https://arxiv.org/abs/2212.09796[Machine learning assisted derivation][Effective low energy model][Metallic magnet]
- https://arxiv.org/abs/2311.10558[Machine learning assisted characterization][Labyrinthine pattern transition]
- https://arxiv.org/abs/2311.13904[3D microstructure characterization][Cu-25Cr solid state sintered alloy][X-ray computed tomography][Machine learning assisted segmentation]
- https://arxiv.org/abs/2402.09128[Machine learning assisted prediction][Organic salt structure properties]
- https://arxiv.org/abs/2409.09583[Machine learning assisted screening][Metal binary alloy][Anode material]
- https://arxiv.org/abs/2311.16351[Microscopic mechanism][Thermal amorphization][ZIF-4][Melting][ZIF-zni][Molecular dynamics][Machine learning techniques]
- https://arxiv.org/abs/2311.17994[Machine learning][Operator content][Critical self-dual Ising-Higgs gauge model]
- https://arxiv.org/abs/2312.01415[Thermally averaged magnetic anisotropy tensor][Machine learning][Gaussian moment]
- https://arxiv.org/abs/2312.03989[Rapid detection][Rare event][In situ X-ray diffraction data][Machine learning]
- https://arxiv.org/abs/2312.06492[Phase transition][LaMnO3][SrRuO3][DFT + U][Machine learning force fields simulation][Phys. Rev. B 108, 235122 (2023)]
- https://arxiv.org/abs/2312.07149[Feature-based prediction][Cross-linked epoxy polymer][Molecular dynamics][Machine learning techniques]
- https://arxiv.org/abs/2312.04660[Application][Machine learning technique][Fast forecast][Aggregation kinetics][Space-inhomogeneous system]
- https://arxiv.org/abs/2312.14552[Machine learning][Structure-guided material][Process design]
- https://arxiv.org/abs/2312.14311[Crystal growth characterization][WSe2 thin film][Machine learning]
- https://arxiv.org/abs/2312.15899[Corrosion-resistant aluminum alloy design][Machine learning][Combined][High-throughput calculation]
- https://arxiv.org/abs/2401.02866[Spin-1/2 kagome Heisenberg antiferromagnet][Machine learning discovery][Spinon pair density wave ground state]
- https://arxiv.org/abs/2401.03044[Machine learning inspired model][Hall effect][Non-collinear magnet]
- https://arxiv.org/abs/2401.06533[Predicting][One-particle density matrix][Machine learning]
- https://arxiv.org/abs/2310.13310[Polarizability model][Simulation][Finite temperature Raman spectra][Machine learning molecular dynamics]
- https://arxiv.org/abs/2312.06963[Enhanced ionic conductivity][Crystallization][Glass-Li3PS4][Machine learning molecular dynamics simulation]
- https://arxiv.org/abs/2401.11427[Correcting force error-induced underestimation][Lattice thermal conductivity][Machine learning molecular dynamics]
- https://arxiv.org/abs/2403.01077[Exploring structural][Electrochemical properties][Li3TiCl6][Machine learning molecular dynamics study]
- https://arxiv.org/abs/2404.11442[Structural properties][Amorphous Na3OCl electrolyte][First-principles][Machine learning molecular dynamics]
- https://arxiv.org/abs/2404.15465[Thermal boundary conductance][Sharp metal-diamond interface][Predicted][Machine learning molecular dynamics]
- https://arxiv.org/abs/2405.04939[Phases transition mechanism][Growth of WS2 and MoS2 layers][Ab initio data driving][Machine learning molecular dynamics]
- https://arxiv.org/abs/2401.10908[Machine learning][Knot topology][Non-Hermitian band braid]
- https://arxiv.org/abs/2401.14808[Raman spectra][Amino acid][Peptides][Machine learning polarizabilities]
- https://arxiv.org/abs/2402.00177[Ab initio amorphous materials database][Empowering machine learning][Decode diffusivity]
- https://arxiv.org/abs/2402.06479[Thermal transport][Glasses][Machine learning driven simulation]
- https://arxiv.org/abs/2402.08884[Machine learning][Density functional theory][Experiment][Understand][Photocatalytic reduction][CO2 by CuPt/TiO2]
- https://arxiv.org/abs/2402.10186[Self-consistent validation][Machine learning electronic structure]
- https://arxiv.org/abs/2402.11204[Predicting superconducting transition temperature][Advanced machine learning][Innovative feature engineering]
- https://arxiv.org/abs/2402.11383[Machine learning][Universal harmonic interatomic potential][Predicting phonon][Crystalline solid]
- https://arxiv.org/abs/2402.11701[Explaining][Machine learning solution][Ising model]
- https://arxiv.org/abs/2403.00259[Deciphering diffuse scattering][Machine learning][Equivariant foundation model][Molten FeO]
- https://arxiv.org/abs/2405.15131[Computational toolkit][Predicting thickness][2D material][Machine learning][Autogenerated dataset][Large language model]
- https://arxiv.org/abs/2403.05119[Estimation][Electronic band gap energy][Material properties][Machine learning]
- https://arxiv.org/abs/2403.10769[Machine learning exchange field][Ab-initio spin dynamics]
- https://arxiv.org/abs/2403.13243[Comparative study][Machine learning models predicting energetics][Interacting defect]
- https://arxiv.org/abs/2403.15579[Efficient first principles][Modeling][Machine learning][Simple representation][High entropy material]
- https://arxiv.org/abs/2404.10903[Superior polymeric gas separation membrane][Designed][Explainable graph machine learning]
- https://arxiv.org/abs/2404.10910[Discovering factorization surface][Quantum spin chain][Machine learning]
- https://arxiv.org/abs/2404.13507[Uncovering obscured phonon dynamics][Powder inelastic neutron scattering][Machine learning]
- https://arxiv.org/abs/2404.14107[PGNAA][Spectral classification][Aluminium and copper alloys][Machine learning]
- https://arxiv.org/abs/2409.04466[Multi-block chemometric approaches][Unsupervised spectral classification][Geological samples]
- https://arxiv.org/abs/2404.17294[Machine learning recognition][Hybrid lead halide perovskite][Perovskite-related structure][Out of X-ray diffraction patterns]
- https://arxiv.org/abs/2405.04876[Accelerating][Prediction][Stacking fault energy][Combining ab initio calculations and machine learning]
- https://arxiv.org/abs/2405.04729[Machine learning][Parameter analysis][Perovskite X-ray detector]
- https://arxiv.org/abs/2405.06104[Fluorescent graphene quantum dots-enhanced machine learning][Accurate detection][Quantification][Hg2+][Fe3+][Real water sample]
- https://arxiv.org/abs/2405.07370[Accelerating QM/MM simulation][Electrochemical interface][Machine learning][Electronic charge densities]
- https://arxiv.org/abs/2405.11393[Optical materials discovery and design][Federated database][Machine learning]
- https://arxiv.org/abs/2405.12143[Machine learning][Predicting ultralow thermal conductivity][High ZT][Complex thermoelectric material]
- https://arxiv.org/abs/2405.18396[Machine learning][Microstructure-informed materials modeling and design]
- https://arxiv.org/abs/2405.19838[Machine learning][Explore high-entropy alloy][Desired enthalpy][Room-temperature hydrogen storage][Prediction][Density functional theory][Experimental data]
- https://arxiv.org/abs/2405.20857[Machine learning conservation law][Dynamical system]
- https://arxiv.org/abs/2405.20946[Fast characterization][Multiplexed single-electron pumps][Machine learning]
- https://arxiv.org/abs/2406.00363[Exploring quantum localization][Machine learning]
- https://arxiv.org/abs/2406.05143[Determining domain][Machine learning model][Kernel density estimate][Application][Materials property prediction]
- https://arxiv.org/abs/2406.06489[Probing out-of-distribution generalization][Machine learning][Material]
- https://arxiv.org/abs/2406.10551[Electron dynamics][Three-dimensional Brillouin zone][Machine learning]
- https://arxiv.org/abs/2406.12904[Meent][Differentiable electromagnetic simulator][Machine learning]
- https://arxiv.org/abs/2406.13389[Unifying mixed gas adsorption][Molecular sieve membrane][MOF][Machine learning]
- https://arxiv.org/abs/2406.14524[High-Tc superconductor candidates][Machine learning]
- https://arxiv.org/abs/2406.15326[LeapFrog][Getting][Jump][Multi-scale materials simulation][Machine learning]
- https://arxiv.org/abs/2406.15316[Discovery][Novel silicon allotrope][Optimized band gap][Enhance solar cell efficiency][Evolutionary algorithm][Machine learning]
- https://arxiv.org/abs/2406.14809[Gas permeability][Diffusivity][Solubility][Polymer][Simulation-experiment data fusion][Multi-task machine learning]
- https://arxiv.org/abs/2406.15515[Machine learning model][Accurately predicting properties][CsPbCl3][Perovskite quantum dot]
- https://arxiv.org/abs/2406.15491[Vibrational entropy][Free energy][Solid lithium][Covariance][Atomic displacement][Machine learning]
- https://arxiv.org/abs/2406.16166[Composite material design][Optimized fracture toughness][Machine learning]
- https://arxiv.org/abs/2406.17197[Machine learning][Screening factor][Soft bond valence approach][Rapid crystal structure estimation]
- https://arxiv.org/abs/2406.17956[Impact][Data bias][Machine learning][Crystal compound synthesizability prediction]
- https://arxiv.org/abs/2407.06975[Optimization][Noncollinear magnetic ordering temperature][Y-type hexaferrite][Machine learning]
- https://arxiv.org/abs/2407.18388[Predicting quantum materials properties][Novel faithful machine learning embedding]
- https://arxiv.org/abs/2407.21228[Describe][Transform][Machine learning][Feature engineering][Grain boundaries][Other variable-sized atom cluster]
- https://arxiv.org/abs/2408.00466[Unlocking Thermoelectric Potential: A Machine Learning Stacking Approach for Half Heusler Alloys]
- https://arxiv.org/abs/2407.21146[Low dimensional fragment-based descriptor][Property prediction][Inorganic material][Machine learning]
- https://arxiv.org/abs/2407.09674[Accelerating high-throughput phonon calculation][Machine learning universal potential]
- https://arxiv.org/abs/2407.12621[Distinguishing isotropic and anisotropic signals][X-ray total scattering][Machine learning]
- https://arxiv.org/abs/2407.17924[Inherent structural descriptor][Machine learning]
- https://arxiv.org/abs/2408.00142[Machine learning][Boosted entropy-engineered synthesis][Stable nanometric solid solution][CuCo alloy][Efficient nitrate reduction to ammonia]
- https://arxiv.org/abs/2408.01141[Machine learning][Topological energy braiding][Non-Bloch band]
- https://arxiv.org/abs/2408.03508[Unsupervised][Self-driving multi-step growth][InAs/GaAs quantum dot][Heterostructure][Machine learning]
- https://arxiv.org/abs/2408.03469[Performance classification][Remaining useful life prediction][Lithium batteries][Machine learning][Early cycle electrochemical impedance spectroscopy measurement]
- https://arxiv.org/abs/2408.03556[Machine learning supported annealing][Prediction][Grand canonical crystal structure]
- https://arxiv.org/abs/2408.08654[Accelerating ab initio melting property calculation][Machine learning][Application][High entropy alloy TaVCrW]
- https://arxiv.org/abs/2408.05135[SPACIER][On-demand polymer design][Fully automated all-atom classical molecular dynamics][Integrated][Machine learning pipeline]
- https://arxiv.org/abs/2408.05850[Machine learning][Characterizing uncertain elastic properties][Fused filament fabricated material][Topology optimization application]
- https://arxiv.org/abs/2408.06654[Advancing nonadiabatic molecular dynamics simulation][Solid][Achieving supreme accuracy][Efficiency][Machine learning]
- https://arxiv.org/abs/2408.09447[Deconvoluting thermomechanical effect][X-ray diffraction data][Machine learning]
- https://arxiv.org/abs/2408.09840[Machine learning][Physics knowledge][Prediction][Survey]
- https://arxiv.org/abs/2408.11949[Machine learning unveil][Materials physical properties][Thermoelectric generators efficiency][Half-Heuslers case]
- https://arxiv.org/abs/2408.13843[Consistent machine learning][Topology optimization][Microstructure-dependent neural network material model]
- https://arxiv.org/abs/2408.15062[Multi-channel machine learning][Nonlocal kinetic energy density functional][Semiconductor]
- https://arxiv.org/abs/2408.15165[Latent Ewald summation][Machine learning][Long-range interaction]
- https://arxiv.org/abs/2408.16499[Predicting topological invariant][Unconventional superconducting pairing][Density of states][Machine learning]
- https://arxiv.org/abs/2409.02633[Predicting miscibility][Binary compound][Machine learning][Genetic algorithm study]
- https://arxiv.org/abs/2409.06085[Differentiable programming across the PDE][Machine learning barrier]
- https://arxiv.org/abs/2409.07987[Evolution][Flat band][MoSe2/WSe2 moiré lattice][Combining machine learning][Band unfolding method]
- https://arxiv.org/abs/2409.07811[Modelling][Nucleate pool boiling][Coated substrate][Machine learning][Empirical approaches]
- https://arxiv.org/abs/2409.11438[Machine learning][Analyzing atomic force microscopy (AFM) image][Generated from polymer blends]
- https://arxiv.org/abs/2409.14573[Decoding][Hidden dynamics][Super-Arrhenius hydrogen diffusion][Multi-principal element alloys][Machine learning]
- https://arxiv.org/abs/2409.18965[Multiscale simulation][Machine learning facilitated design][Two-dimensional nanomaterials-based tunnel field-effect transistor][Review]
- https://arxiv.org/abs/2409.18822[Automated quantum system modeling][Machine learning]
- https://arxiv.org/abs/2410.01213[Versatile machine learning workflow][High-throughput analysis][Supported metal catalyst particle]
- https://arxiv.org/abs/2410.05604[Accelerating][Discovery][Low-energy structure configuration][Computational approach][Integrates first-principles calculation][Monte Carlo sampling][Machine learning]
- https://arxiv.org/abs/2410.05574[Machine learning inversion][Scattering][Mechanically driven polymer]
- https://arxiv.org/abs/2410.11392[Investigating data hierarchies][Multifidelity machine learning][Excitation energies]
- https://arxiv.org/abs/2410.12007[Machine learning][Ising model][Spherical Fibonacci lattice]
- https://arxiv.org/abs/2410.13645[Automated model discovery][Tensional homeostasis][Constitutive machine learning][Growth and remodeling]
- https://arxiv.org/abs/2410.13711[Automated classification][Individual atom][Surface][Machine learning]
- https://arxiv.org/abs/2410.13141[Federated scientific machine learning][Approximating function][Solving differential equation][Data heterogeneity]
- https://arxiv.org/abs/2112.02287[BenchML][Extensible pipelining framework][benchmarking representation][Material][Molecule][Scale]
- https://arxiv.org/abs/2104.01352[Learning Rule][Materials Properties and Functions]
- https://arxiv.org/abs/2101.00067[Screening and understanding Li adsorption][2-dimensional metallic material][Learning physics]
- https://arxiv.org/abs/2206.15457[PhySRNet][Physics informed super-resolution network][Application][Computational solid mechanics]
- https://arxiv.org/abs/2008.08781[Boron cage effect][Nd-Fe-B crystal structure][Stability]
- https://arxiv.org/abs/2007.07523[Atomistic structure learning algorithm][Surrogate energy model relaxation]
- https://arxiv.org/abs/2002.11952[Autonomous robotic nanofabrication][Reinforcement learning]
- https://arxiv.org/abs/2308.07897[Probabilistic phase labeling][Lattice refinement][Autonomous material research]
- https://arxiv.org/abs/2408.00229[Invariant discovery][Features across multiple length scales][Application][Microscopy and autonomous materials characterization]
- https://arxiv.org/abs/2009.06739[Predictive synthesis][Quantum material][Probabilistic reinforcement learning]
- https://arxiv.org/abs/2105.01079[Experimental deep reinforcement learning][Error-robust gateset design][Superconducting quantum computer]
- https://arxiv.org/abs/2209.11259[Computational discovery][Energy-efficient heat treatment][Microstructure design][Deep reinforcement learning]
- https://arxiv.org/abs/2005.12759[Classifying global state preparation][Deep reinforcement learning]
- https://arxiv.org/abs/2203.06975[Precise atom manipulation][Deep reinforcement learning]
- https://arxiv.org/abs/2204.06288[Automated atomic silicon quantum dot circuit design][Deep reinforcement learning]
- https://arxiv.org/abs/2009.14825[Deep reinforcement learning][Efficient measurement][Quantum device]
- https://arxiv.org/abs/2210.11931[Deep reinforcement learning][Inverse inorganic materials design]
- https://arxiv.org/abs/2312.05445[Molecular autonomous pathfinder][Deep reinforcement learning]
- https://arxiv.org/abs/2404.05905[Computing transition pathway][Rare event][Deep reinforcement learning]
- https://arxiv.org/abs/2407.12453[Estimating reaction barrier][Deep reinforcement learning]
- https://arxiv.org/abs/2312.03687[MatterGen][Generative model][Inorganic materials design]
- https://arxiv.org/abs/2110.05260[Designing composite][Target effective Young's modulus][Reinforcement learning]
- https://arxiv.org/abs/2204.04785[Driving black-box][Quantum thermal machine][Optimal power/efficiency trade-off][Reinforcement learning]
- https://arxiv.org/abs/2209.04467[Dynamic zoom-in detection][Exfoliated two-dimensional crystal][Reinforcement learning]
- https://arxiv.org/abs/2212.11651[Approximate autonomous quantum error correction][Reinforcement learning][Phys. Rev. Lett. 131, 050601 (2023)]
- https://arxiv.org/abs/2307.05394[Reinforcement learning-guided long-timescale simulation][Hydrogen transport][Metal]
- https://arxiv.org/abs/2310.02902[Searching][High-value molecule][Reinforcement learning][Transformer]
- https://arxiv.org/abs/2311.17519[Reinforcement learning][Thermal fluctuation][Nano-scale]
- https://arxiv.org/abs/2402.00972[Closure discovery][Coarse-grained partial differential equation][Multi-agent reinforcement learning]
- https://arxiv.org/abs/2402.10559[Discovery][Exchange-only gate sequence][CNOT][Record-low gate time][Reinforcement learning]
- https://arxiv.org/abs/2404.18774[Self-training superconducting neuromorphic circuit][Reinforcement learning rule]
- https://arxiv.org/abs/2407.15872[Reinforcement learning strategy][Automate and accelerate h/p-multigrid solvers]
- https://arxiv.org/abs/2408.09524[Enhancing quantum memory lifetime][Measurement-free local error correction][Reinforcement learning]
- https://arxiv.org/abs/2302.13380[Closed-loop error correction learning][Aaccelerate][Experimental discovery][Thermoelectric material]
- https://arxiv.org/abs/1812.00085[NOMAD 2018 Kaggle Competition][Solving materials science challenges][Crowd sourcing]
- https://arxiv.org/abs/2205.15686[NOMAD][Artificial-intelligence toolkit][Turning materials-science data][Knowledge][Understanding]
- https://arxiv.org/abs/2403.10470[MADAS][Python framework][Assessing similarity][Materials-science data]
- https://arxiv.org/abs/2101.01773[Learning the Crystal Structure Genome][Property classification]
- https://arxiv.org/abs/2105.04085[Bond-length distribution][Classified][Coordination environment]
- https://arxiv.org/abs/2103.15855[Learning algorithm][Emergent scaling behavior][Classifying phase transition]
- https://arxiv.org/abs/2308.16621[Meta-analysis][Literature data][Metal additive manufacturing][What can we (and the machine) learn from reported data?]
- https://arxiv.org/abs/2205.05794[Deep-learned generator][Porosity distribution][Metal additive manufacturing]
- https://arxiv.org/abs/2104.10242[Accelerating materials discovery][Bayesian optimization][Graph deep learning]
- https://arxiv.org/abs/2202.02450[Universal graph deep learning][Interatomic potential][Periodic table]
- https://arxiv.org/abs/2302.03331[Graph deep learning][Accelerated efficient crystal structure search][Feature extraction]
- https://arxiv.org/abs/2409.00957[Data-efficient construction][High-fidelity graph deep learning][Interatomic potential]
- https://arxiv.org/abs/1706.06480[Advanced steel microstructure classification][Deep learning]
- https://arxiv.org/abs/1709.02298[The face of crystals][Insightful classification][Deep learning]
- https://arxiv.org/abs/2108.09523[Automating crystal-structure phase mapping][Combining deep learning][Constraint reasoning]
- https://arxiv.org/abs/1712.06113[SchNet][Deep learning architecture][Molecules][Materials]
- https://arxiv.org/abs/1801.05860[Deep learning][Atomically resolved scanning transmission electron microscopy Image][Chemical identification][Tracking local transformation]
- https://arxiv.org/abs/1802.10518[Mapping mesoscopic phase evolution][e-beam induced transformation][Deep learning][Atomically resolved image]
- https://arxiv.org/abs/1805.02791[Microstructural materials Design][Deep adversarial learning]
- https://arxiv.org/abs/1803.05381[Deep learning Analysis][Defect and phase evolution][Electron beam induced transformations][WS2]
- https://arxiv.org/abs/1805.10503[Deep learning][Topological invariant]
- https://arxiv.org/abs/1809.03704[General resolution enhancement method][Atomic Force Microscopy][Deep learning]
- https://arxiv.org/abs/1809.10860[Deep learning][Bandgap][Topologically doped graphene]
- https://arxiv.org/abs/1811.08928[Deep Learning][DFT]
- https://arxiv.org/abs/1811.09724[3D deep learning][Voxelized atomic configuration][Modeling atomistic potential][Complex solid-solution alloy]
- https://arxiv.org/abs/2404.16524[3D deep learning][Enhanced atom probe tomography analysis][Nanoscale microstructure]
- https://arxiv.org/abs/1901.10669[Predicting the mechanical response][Oligocrystal][Deep learning]
- https://arxiv.org/abs/1902.06876[Reconstruction of 3-D atomic distortion][Electron microscopy][Deep learning]
- https://arxiv.org/abs/2307.04638[DeePTB][Deep learning-based tight-binding approach][Ab initio accuracy]
- https://arxiv.org/abs/2110.13720[Deep DIC][Deep learning-based digital image correlation][End-to-end displacement][Strain measurement]
- https://arxiv.org/abs/1904.05433[Phase segmentation][Atom-probe tomography][Deep learning-based edge detection]
- https://arxiv.org/abs/2405.10325[Uncertainty][Exploration][Deep learning-based atomistic model][Screening molten salt properties and compositions]
- https://arxiv.org/abs/2408.15681[Towards a unified benchmark and framework][Deep learning-based prediction][Nuclear magnetic resonance chemical shift]
- https://arxiv.org/abs/1905.10730[Deep learning-enhanced variational Monte Carlo method][Quantum many-body physics]
- https://arxiv.org/abs/1906.02130[Predicting compressive strength][Consolidated molecular solid][Computer vision][Deep learning]
- https://arxiv.org/abs/1906.11220[Deep learning][Enabled fast optical characterization][Two-dimensional material]
- https://arxiv.org/abs/1909.04648[Deep learning][Automated classification][characterization][Amorphous material]
- https://arxiv.org/abs/1909.04784[Deep learning][Topological phase transition][Entanglement aspect]
- https://arxiv.org/abs/1911.12566[Deep Learning][Inverse design][Mid-infrared graphene plasmon]
- https://arxiv.org/abs/2011.12603[Deep learning][Nano-photonics][Inverse design and beyond]
- https://arxiv.org/abs/2108.12019[Generative deep learning][Inverse design][High-entropy refractory alloy]
- https://arxiv.org/abs/2409.19124[Generative deep learning][Inverse design of materials]
- https://arxiv.org/abs/2312.15136[Towards end-to-end structure solution][Information-compromised diffraction data][Generative deep learning]
- https://arxiv.org/abs/2109.03114[Deep learning][Modeling][Inverse design][Radiative heat transfer]
- https://arxiv.org/abs/1910.00617[Predicting materials properties][Without crystal structure][Deep representation learning][Stoichiometry]
- https://arxiv.org/abs/1910.10467[Deep learning][Supersampled scanning transmission electron microscopy]
- https://arxiv.org/abs/1910.13551[Deep learning][Optoelectronic][Organic semiconductor]
- https://arxiv.org/abs/1912.03296[Rapid exploration][Topological band structure][Deep learning]
- https://arxiv.org/abs/1912.09027[Density functional theory][Deep-learning][Accelerate data analytics][Scanning tunneling microscopy]
- https://arxiv.org/abs/1912.05916[Deep-learning estimation][Band gap][Reading-periodic-table method][Periodic convolution layer]
- https://arxiv.org/abs/2209.13026[Electron energy loss spectroscopy database synthesis][Automation][Core-loss edge recognition][Deep-learning neural network]
- https://arxiv.org/abs/2211.10604[Deep-learning electronic-structure calculation][Magnetic superstructure]
- https://arxiv.org/abs/2005.09634[Automated][Copper alloy grain size evaluation][Deep-learning CNN]
- https://arxiv.org/abs/2010.09547[Deep-learning interatomic potential][Irradiation damage simulation][MoS2][Ab initial accuracy]
- https://arxiv.org/abs/2403.03515[Lattice thermal conductivity][Mechanical properties][Single-layer penta-NiN2][Deep-learning interatomic potential]
- https://arxiv.org/abs/2205.08366[Deep-learning model][Fast Prediction][Vacancy formation][Diverse material]
- https://arxiv.org/abs/2409.15784[Deep-learning real-time phase retrieval][Imperfect diffraction pattern][X-ray free-electron laser]
- https://arxiv.org/abs/2410.13594[Deep-learning recognition][Tracking][Individual nanotube][Low-contrast microscopy video]
- https://arxiv.org/abs/1910.12750[Deep-learning-based image segmentation][Integrated][Optical microscopy][Automatically searching][Two-dimensional material]
- https://arxiv.org/abs/2205.11407[Deep-learning-based prediction][Nanoparticle phase transition][in situ transmission electron microscopy]
- https://arxiv.org/abs/2401.14588[Deep-learning-based prediction][Tetragonal --> cubic transition][Davemaoite]
- https://arxiv.org/abs/2205.00060[Designing high-Tc superconductor][BCS-inspired screening][Density functional theory][Deep-learning]
- https://arxiv.org/abs/2311.14557[Augmentation][Scarce data][New approach][Deep-learning modeling][Composites]
- https://arxiv.org/abs/2401.17015[DeepH-2][Enhancing deep-learning electronic structure][Equivariant local-coordinate transformer]
- https://arxiv.org/abs/2212.04457[Spatio-temporal super-resolution][Dynamical system][Physics-informed deep-learning]
- https://arxiv.org/abs/2302.06389[Deep-learning][Quantitative structural characterization][Additive manufacturing]
- https://arxiv.org/abs/2404.06449[Deep-learning database][Density functional theory Hamiltonian][Twisted materials]
- https://arxiv.org/abs/2405.05975[Deep-learning design][Graphene metasurface][Quantum control][Dirac electron holography]
- https://arxiv.org/abs/2405.17089[Improved penalty-based excited-state][Variational Monte Carlo approach][Deep-learning ansatzes]
- https://arxiv.org/abs/2406.14585[Deep-learning-assisted reconfigurable metasurface antenna][Real-time holographic beam steering][Nanophotonics 12.13 (2023): 2415-2423]
- https://arxiv.org/abs/1912.11582[Deep learning][Aided rational design][Oxide glass]
- https://arxiv.org/abs/2001.02050[Deep learning][Surrogate interacting Markov chain Monte Carlo][Full wave inversion scheme][Materials quantification]
- https://arxiv.org/abs/2001.08233[Deep learning enabled strain mapping][Single-atom defect][2D transition metal dichalcogenide][Sub-picometer precision]
- https://arxiv.org/abs/2002.04716[Robust multi-scale multi-feature deep learning][Atomic and defect identification][STM][H-Si(100) 2x1 surface]
- https://arxiv.org/abs/2002.06763[Optimization][Validation][Deep learning][CuZr atomistic potential][Robust][Application][Crystalline][Amorphous][Near-DFT Accuracy]
- https://arxiv.org/abs/2002.07055[Optical lattice experiment][Unobserved conditions and scales][Generative adversarial deep learning]
- https://arxiv.org/abs/2004.01396[Generative adversarial network][Crystal structure prediction]
- https://arxiv.org/abs/2310.07836[Revolutionising inverse design][Magnesium alloy][Generative adversarial network]
- https://arxiv.org/abs/2002.09039[Deep learning][Interface structure][4D STEM data][Cation intermixing vs. roughening]
- https://arxiv.org/abs/2002.10632[Genetic algorithm-guided deep learning][Grain boundary diagram][Addressing][Challenge][Five degrees of freedom]
- https://arxiv.org/abs/2003.12259[0.71-][Resolution electron tomography][Deep learning][Information recovery]
- https://arxiv.org/abs/2003.12402[Fast design][Plasmonic metasurface][Enabled by deep learning]
- https://arxiv.org/abs/2004.04814[Deep learning][Synthetic microstructure generation][Mmaterials-by-design framework][Heterogeneous energetic material]
- https://arxiv.org/abs/2005.01144[Deep learning][Understand and mitigate][Qubit noise environment]
- https://arxiv.org/abs/2005.03759[DeePore][Deep learning workflow][Rapid and comprehensive characterization][Porous material]
- https://arxiv.org/abs/2401.17788[Prompt-engineered large language model][Deep learning workflow][Materials classification]
- https://arxiv.org/abs/2005.10672[Insight][One-body density matrices][Deep learning]
- https://arxiv.org/abs/2006.03532[Quantifying][Dynamics][Protein self-organization][Deep learning analysis][Atomic force microscopy data]
- https://arxiv.org/abs/2006.09441[Real-time 3D nanoscale coherent imaging][Physics-aware deep learning]
- https://arxiv.org/abs/2009.00661[Transfer and confusion deep learning][Frustrated spin system]
- https://arxiv.org/abs/2009.08328[Review][Deep learning][Electron microscopy]
- https://arxiv.org/abs/2009.11810[Deep learning][Enabled design][Complex transmission matrix][Universal optical component]
- https://arxiv.org/abs/2010.16082[Deep learning][Accurate force field][Ferroelectric HfO2]
- https://arxiv.org/abs/2011.10505[Synthetic image rendering][Annotation problem][Deep learning][Nanoparticle segmentation]
- https://arxiv.org/abs/2011.10227[StressNet][Deep learning][Predict stress][Fracture propagation][Brittle material]
- https://arxiv.org/abs/2011.10883[Supervised deep learning prediction][Formation enthalpy][Full set][Configuration][Complex phase][σ−phase as an example]
- https://arxiv.org/abs/2012.01478[Leveraging uncertainty][Deep learning][Trustworthy materials discovery workflow]
- https://arxiv.org/abs/2012.03184[dPOLY][Deep learning][Polymer phase][Phase transition]
- https://arxiv.org/abs/2012.05322[Deep learning segmentation][Complex feature][Atomic-resolution phase contrast transmission electron microscopy image]
- https://arxiv.org/abs/2012.06056[Predicting orientation-dependent plastic susceptibility][Static structure][Amorphous solid][Deep learning]
- https://arxiv.org/abs/2012.09093[TEMImageNet][AtomSegNet][Deep learning training library][High-precision][Atom segmentation][Localization][Denoising][Super-resolution processing][Atom-resolution scanning TEM Image]
- https://arxiv.org/abs/2101.01178[Advance][Electron microscopy][Deep learning]
- https://arxiv.org/abs/2102.01620[AlphaCrystal][Contact map based crystal structure prediction][Deep learning]
- https://arxiv.org/abs/2102.12009[Deep learning order parameter][Polymer phase transition]
- https://arxiv.org/abs/2103.12526[Effectiveness of data augmentation][Porous substrate][Nanowire][Fiber][Tip image][Deep learning intelligence]
- https://arxiv.org/abs/2104.03574[Deep learning][Topological phase transition][Entanglement aspect][Two-dimensional chiral p-wave superconductor]
- https://arxiv.org/abs/2104.04392[Deep learning][Visualization][Novelty detection][Large X-ray diffraction dataset]
- https://arxiv.org/abs/2104.05408[Orbital-free density functional theory][Small dataset][Deep learning]
- https://arxiv.org/abs/2105.03870[Deep learning][Topological phase transition][Entanglement aspect][Unsupervised way]
- https://arxiv.org/abs/2105.07125[Disentangling ferroelectric wall dynamics][Identification][Pinning mechanism][Deep learning]
- https://arxiv.org/abs/2105.09866[Deep learning][Dielectric quasi-cubic particle][Uniform electric field]
- https://arxiv.org/abs/2105.10564[Deep learning prediction][Stress field][Additively manufactured metal][Intricate defect network]
- https://arxiv.org/abs/2106.12730[Deep learning][Crystal plasticity][Preconditioning approach][Accurate orientation evolution prediction]
- https://arxiv.org/abs/2107.13711[Bio-inspired vascularized electrode][High-performance fast-charging battery][Designed by deep learning]
- https://arxiv.org/abs/2108.01057[Deep learning][Deformation-dependent conductance][Thin film][Nanobubble][Graphene]
- https://arxiv.org/abs/2108.07222[Deep learning analysis][Polaritonic waves image]
- https://arxiv.org/abs/2108.07614[Electronic response quantities of solids][Deep Learning]
- https://arxiv.org/abs/2108.08882[Deep learning][Automatic defect analysis framework][In-situ TEM Ion Irradiation]
- https://arxiv.org/abs/2108.08883[Multi defect detection][Analysis][Electron microscopy image][Deep learning]
- https://arxiv.org/abs/2108.13830[Bridging the gap][Deep learning][Frustrated quantum spin system][Extreme-scale simulation][New generation of Sunway supercomputer]
- https://arxiv.org/abs/2109.07104[Accurate force field][Two-dimensional ferroelectrics][Deep learning]
- https://arxiv.org/abs/2109.14053[AutoPhaseNN][Unsupervised physics-aware][Deep learning][3D nanoscale coherent imaging]
- https://arxiv.org/abs/2110.02109[Deep learning][Modelling][Complex aperiodic plasmonic metasurface][Arbitrary size]
- https://arxiv.org/abs/2110.08244[Performance][Success][Limitation][Deep learning][Semantic segmentation][Multiple defects][Transmission electron micrograph]
- https://arxiv.org/abs/2103.16664[Probabilistic deep learning approach][Automate][Interpretation][Multi-phase diffraction spectra]
- https://arxiv.org/abs/2110.12958[Probabilistic deep learning approach][Targeted hybrid organic-inorganic perovskite]
- https://arxiv.org/abs/2111.05271[Stress field prediction][Fiber-reinforced composite material][Deep learning approach]
- https://arxiv.org/abs/2201.07342[Deep learning approach][Semantic segmentation][Unbalanced data][Electron tomography][Catalytic material]
- https://arxiv.org/abs/2203.06820[Edge detection][Image filter algorithm][Spectroscopic analysis][Deep learning application]
- https://arxiv.org/abs/2303.16412[Comprehensive][Versatile][Multimodal deep learning approach][Predicting][Diverse properties][Advanced material]
- https://arxiv.org/abs/2304.09606[Screening spin lattice interaction][Deep learning approach]
- https://arxiv.org/abs/2309.09355[Structure to property][Chemical element embedding][Deep learning approach][Accurate prediction][Chemical properties]
- https://arxiv.org/abs/2310.06852[DeepCrysTet][Deep learning approach][Tetrahedral mesh][Predicting properties][Crystalline material]
- https://arxiv.org/abs/2310.15188[Deep learning approaches][Dynamic mechanical analysis][Viscoelastic fiber composite]
- https://arxiv.org/abs/1802.03008[Deep learning approach][Identify local structure][Atomic-resolution transmission electron microscopy image]
- https://arxiv.org/abs/1901.00915[Deep learning approach][Structural analysis of proteins]
- https://arxiv.org/abs/2003.11505[Deep learning approach][Determining the chiral indices][Carbon nanotube][High-resolution transmission electron microscopy image]
- https://arxiv.org/abs/2409.07721[Deep learning approach][Search for superconductors][Electronic bands]
- https://arxiv.org/abs/2112.01971[Dynamic fracture][Bicontinuously nanostructured copolymer][Deep learning analysis][Big-data-generating experiment]
- https://arxiv.org/abs/2112.03072[Deep learning][Automated phase segmentation][EBSD map][Dual phase steel microstructure]
- https://arxiv.org/abs/2012.09314[Computational discovery][New 2D material][Deep learning generative model]
- https://arxiv.org/abs/2112.03528[Physics guided deep learning generative model][Crystal materials discovery]
- https://arxiv.org/abs/2403.10846[Deep learning generative model][Crystal structure prediction]
- https://arxiv.org/abs/2112.03364[Scalable geometric deep learning][Molecular graph]
- https://arxiv.org/abs/2303.10140[Geometric deep learning][Molecular crystal structure prediction]
- https://arxiv.org/abs/2409.05169[Learning polycrystal plasticity][Mesh-based subgraph geometric deep learning]
- https://arxiv.org/abs/2112.04977[Bringing atomistic deep learning][Prime time]
- https://arxiv.org/abs/2112.14429[Leveraging large-scale computational database][Deep learning][Accurate prediction]
- https://arxiv.org/abs/2201.13306[Deep learning][Disordered topological insulator][Entanglement spectrum]
- https://arxiv.org/abs/2202.00204[Disentangling multiple scattering][Deep learning][Application][Strain mapping][Electron diffraction pattern]
- https://arxiv.org/abs/2202.00574[Identifying Pauli spin blockade][Deep learning]
- https://arxiv.org/abs/2202.12611[Phase object reconstruction][4D-STEM][Deep learning]
- https://arxiv.org/abs/2202.13268[Deep learning][Functional renormalization group][Phys. Rev. Lett. 129, 136402 (2022)]
- https://arxiv.org/abs/2203.16676[Exploring][Hysteresis properties][Nanocrystalline permanent magnet][Deep learning]
- https://arxiv.org/abs/2204.01769[Deep learning][Rare-event rational design][3D printed multi-material][Mechanical metamaterial]
- https://arxiv.org/abs/2401.00065[Accelerating process development][3D printing][New metal alloy]
- https://arxiv.org/abs/2204.04740[Melting temperature prediction][First principles][Deep learning]
- https://arxiv.org/abs/2204.08157[Deep learning][Coherent diffractive imaging]
- https://arxiv.org/abs/2205.05426[RustSEG][Automated segmentation][Corrosion][Deep learning]
- https://arxiv.org/abs/2205.09075[Predicting failure characteristics][Structural material][Deep learning][nondestructive void topology]
- https://arxiv.org/abs/2206.04272[STEM image analysis][Deep learning][Identification][Vacancy defect][Polymorph][MoS2]
- https://arxiv.org/abs/2206.14670[Deep learning][Spin-orbit torque characterization][Projected vector field magnet]
- https://arxiv.org/abs/2205.08367[Deep learning density functional][Gradient descent optimization]
- https://arxiv.org/abs/2407.14379[Deep learning density functional theory Hamiltonian][Real space]
- https://arxiv.org/abs/2401.17892[Deep-learning density functional][Perturbation theory][Phys. Rev. Lett. 132, 096401 (2024)]
- https://arxiv.org/abs/2403.09788[Solving deep-learning density functional theory][Variational autoencoder]
- https://arxiv.org/abs/2406.10536[Universal materials model][Deep-learning density functional theory Hamiltonian]
- https://arxiv.org/abs/2206.11457[Exploring physics][Ferroelectric domain wall][Real time][Deep learning][Scanning probe microscopy]
- https://arxiv.org/abs/1905.03928[Deep learning model][Atomic structures prediction][X-ray absorption spectroscopic data]
- https://arxiv.org/abs/2302.00261[Deep learning model][Inverse design][Semiconductor heterostructure][Desired electronic band structure]
- https://arxiv.org/abs/1812.01995[Deep learning model][Finding new superconductors]
- https://arxiv.org/abs/1912.06077[Understanding important features][Deep learning model][Transmission electron microscopy image segmentation]
- https://arxiv.org/abs/2105.12638[Predicting aqueous solubility][Organic molecule][Deep learning model][Varied molecular representation]
- https://arxiv.org/abs/2203.14326[DeepXRD][Deep learning model][Predicting][XRD spectrum][Materials composition]
- https://arxiv.org/abs/2211.11543[Leveraging orbital information][Atomic feature][Deep learning model]
- https://arxiv.org/abs/2211.04561[Physics-aware deep learning model][Energy localization][Multiscale shock-to-detonation simulation][Heterogeneous energetic material]
- https://arxiv.org/abs/2207.00243[Bridging fidelities][Predict nanoindentation tip radii][Interpretable deep learning model][JOM volume 74, pages 21952205 (2022)]
- https://arxiv.org/abs/2207.05013[Boosting heterogeneous catalyst discovery][Structurally constrained deep learning model]
- https://arxiv.org/abs/2207.10173[Benchmark test][Atom segmentation deep learning model][Consistent dataset]
- https://arxiv.org/abs/2403.08596[Patching-based deep learning model][Inpainting][Bragg coherent diffraction pattern][Detectors' gaps]
- https://arxiv.org/abs/2408.06156[Graph-based descriptor][Condensed matter]
- https://arxiv.org/abs/2002.11315[Assessing graph-based deep learning Model][Predicting flash point]
- https://arxiv.org/abs/2309.14638[Deep charge][Deep learning model][Electron density][One-shot density functional theory calculation]
- https://arxiv.org/abs/2310.01350[Peridynamic-informed deep learning model][Brittle damage prediction]
- https://arxiv.org/abs/2402.10931[Enabling discovery][Enhanced generalisability][Deep learning model]
- https://arxiv.org/abs/2401.03004[SAPNet][Deep learning model][Identification][Single-molecule peptide post-translational modification][Surface enhanced Raman spectroscopy]
- https://arxiv.org/abs/2405.18944[Predicting many properties][Crystal][Single deep learning model]
- https://arxiv.org/abs/2408.01558[Accelerating domain-aware electron microscopy analysis][Deep learning model][Synthetic data][Image-wide confidence scoring]
- https://arxiv.org/abs/2207.08681[Deep learning potential][Mean force][Polymer grafted nanoparticle]
- https://arxiv.org/abs/2210.03430[Monitoring MBE substrate deoxidation][RHEED image-sequence analysis][Deep learning]
- https://arxiv.org/abs/2210.06310[Determining band structure parameters][Two-dimensional material][Deep learning]
- https://arxiv.org/abs/2210.06622[When does deep learning fail and how to tackle it?][Critical analysis][Polymer sequence-property surrogate model]
- https://arxiv.org/abs/2112.10254[Inverse deep learning method][Benchmark][Artificial electromagnetic material design]
- https://arxiv.org/abs/2001.06762[Development of interatomic potential][Al-Tb alloy][Deep learning method]
- https://arxiv.org/abs/2008.00442[Identifying][Elastic isotropy][Architectured material][Deep learning method]
- https://arxiv.org/abs/2110.14820[Recent advance][Application][Deep learning method][Materials Science]
- https://arxiv.org/abs/2204.07816[2^1296][Exponentially complex quantum many-body simulation][Scalable deep learning method]
- https://arxiv.org/abs/2210.10494[Spectroscopic data de-noising][Training-set-free deep learning method]
- https://arxiv.org/abs/2210.11200[Removing grid structure][Angle-resolved photoemission spectra][Deep learning method]
- https://arxiv.org/abs/2311.07059[Application][Deep learning method][Study of magnetic phenomena]
- https://arxiv.org/abs/2402.11434[Deep learning method][Hamiltonian parameter estimation][Magnetic domain image generation][Twisted van der Waals magnet]
- https://arxiv.org/abs/2404.06206[Deep learning method][Computing committor function][Adaptive sampling]
- https://arxiv.org/abs/2404.10891[Deep learning method][Colloidal silver nanoparticle concentration][Size distribution determination][UV-Vis extinction spectra]
- https://arxiv.org/abs/2211.01490[Deep learning Hamiltonian][Disordered image data][Quantum material]
- https://arxiv.org/abs/2211.09050[Deep learning][Spatial densities][Inhomogeneous correlated quantum system]
- https://arxiv.org/abs/2211.09866[Fast uncertainty estimate][Deep learning interatomic potential]
- https://arxiv.org/abs/2404.16187[Deep learning interatomic potential][Molecular structural ordering][Macroscale properties][Polyacrylonitrile (PAN) polymer]
- https://arxiv.org/abs/2406.09011[Finite-temperature properties][Antiferroelectric perovskite PbZrO3][Deep learning interatomic potential]
- https://arxiv.org/abs/2211.15895[Composition based oxidation state prediction][Deep learning]
- https://arxiv.org/abs/2212.02643[Architected material][Mechanical compression][Design][Simulation][Deep learning][Experimentation]
- https://arxiv.org/abs/2105.07485[AtomAI][Deep learning framework][Analysis][Image][Spectroscopy data][(Scanning) transmission electron microscopy and Beyond]
- https://arxiv.org/abs/2007.04898[Difference-based deep learning framework][Stress prediction][Heterogeneous media]
- https://arxiv.org/abs/2212.14601[Accelerated multiscale mechanics modeling][Deep learning framework]
- https://arxiv.org/abs/2304.00616[Robust deep learning framework][Constitutive-relation modeling]
- https://arxiv.org/abs/2307.04121[Deep learning framework][Solving hyperbolic partial differential equation][Part I]
- https://arxiv.org/abs/2407.20384[Unified deep learning framework][Many-body quantum chemistry][Green's function]
- https://arxiv.org/abs/2407.20251[Uncertainty-aware deep learning framework-based robust design optimization][Metamaterial unit]
- https://arxiv.org/abs/2409.19552[Universal deep learning framework][Materials X-ray absorption spectra]
- https://arxiv.org/abs/2212.06444[Predicting thermoelectric transport properties][Composition][Attention-based deep learning]
- https://arxiv.org/abs/2301.05875[Diatom-inspired architected material][Language-based deep learning][Perception][Transformation][Manufacturing]
- https://arxiv.org/abs/2212.13809[Atomic force microscopy simulation][CO-functionalized tip][Deep learning]
- https://arxiv.org/abs/2302.08221[Efficient hybrid density functional calculation][Deep learning]
- https://arxiv.org/abs/2303.05869[Three-dimensional damage characterisation][Dual phase steel][Deep learning]
- https://arxiv.org/abs/2303.17345[Mapping microstructure][Shock-induced temperature field][Deep learning]
- https://arxiv.org/abs/2304.02048[Deep learning][Automated experimentation][Scanning transmission electron microscopy]
- https://arxiv.org/abs/2304.04986[Deep learning][Experimental electrochemistry][Battery cathode][Diverse composition]
- https://arxiv.org/abs/2304.08616[Exploring exotic configuration][Anomalous feature][Deep learning][Application][Classical][Quantum-classical hybrid anomaly detection]
- https://arxiv.org/abs/2305.02917[Bridging theory][Experiment][Digital twin][Deep learning segmentation][Defect][Monolayer MX2 phase]
- https://arxiv.org/abs/2305.05634[Deep learning][Genetic algorithm framework][Tailoring mechanical properties][Inverse microstructure optimization]
- https://arxiv.org/abs/2305.06981[Local structure][Thermodynamics][Melting curve][Boron phosphide][High pressure][Deep learning-driven ab initio simulation]
- https://arxiv.org/abs/2409.02952[Deep learning-driven evaluation][Prediction][Ion-doped NASICON material][Enhanced solid-state battery performance]
- https://arxiv.org/abs/2305.07790[Automated grain boundary][Segmentation][Microstructural Analysis][347H stainless steel][Deep learning][Multimodal microscopy]
- https://arxiv.org/abs/2305.15370[Deep learning][Non-local and scalable energy functionals][Quantum Ising model]
- https://arxiv.org/abs/2305.19516[Deep learning inter-atomic potential][Irradiation damage][3C-SiC]
- https://arxiv.org/abs/2305.19302[Smooth][Exact rotational symmetrization][Deep learning][Point cloud]
- https://arxiv.org/abs/2306.00797[Microstructure quality control][Steel][Deep learning]
- https://arxiv.org/abs/2306.08285[Bonding-aware materials representation][Deep learning atomistic model]
- https://arxiv.org/abs/2405.04967[MatterSim][Deep learning atomistic model][Across elements, temperatures and pressures]
- https://arxiv.org/abs/2408.02292[Learning atoms][Crystal structure]
- https://arxiv.org/abs/2307.04283[Deep learning][CALPHAD modeling][Universal parameter learning][Solely][Chemical formula]
- https://arxiv.org/abs/2307.06322[Deep learning][Crystalline defect][TEM image][Solution][Problem]["Never Enough Training Data"]
- https://arxiv.org/abs/2308.11221[Ferroelectric domain][Switching dynamics][Curved In2Se3][First principles][Deep learning molecular dynamics simulation]
- https://arxiv.org/abs/2309.01995[Photonic structures optimization][Highly data-efficient deep learning][Application][Nanofin][Annular groove phase mask]
- https://arxiv.org/abs/2310.01117[Predicting emergence][Crystal][Amorphous matter][Deep learning]
- https://arxiv.org/abs/2310.08302[Deep learning][EELS hyperspectral images unmixing][Autoencoder]
- https://arxiv.org/abs/2310.08618[Deep learning][Nano-photonic material][The solution to everything!?]
- https://arxiv.org/abs/2310.18907[Topological, or Non-topological?][Deep learning based prediction]
- https://arxiv.org/abs/2311.11665[Enhancing crystal structure prediction][Leveraging computational][Experimental data][Combination][Deep learning][Optimization algorithm]
- https://arxiv.org/abs/2311.13170[Iterative deep learning procedure][Determining electron scattering cross-section][Transport coefficient]
- https://arxiv.org/abs/2311.14936[Single-image][Deep learning][Precise atomic defects identification]
- https://arxiv.org/abs/2312.02871[Attention-enhanced neural differential equation][Physics-informed deep learning][Ion transport]
- https://arxiv.org/abs/2405.08580[Importance][Hyper-parameter optimization][Training][Physics-informed deep learning network]
- https://arxiv.org/abs/2409.06560[Primer on variational inference][Physics-informed deep generative modelling]
- https://arxiv.org/abs/2312.05160[Detecting atomic scale surface defect][STM][TMDs][Ensemble deep learning]
- https://arxiv.org/abs/2312.09133[Deep learning][Plasma plume image sequences][Anomaly detection][Prediction][Growth kinetics][Pulsed laser deposition]
- https://arxiv.org/abs/2401.02021[Accelerated band offset prediction][Semiconductor interface][DFT][Deep learning]
- https://arxiv.org/abs/2401.16611[Accelerating superconductor discovery][Tempered deep learning][Electron-phonon spectral function]
- https://arxiv.org/abs/2401.17513[PNP ion channel deep learning solver][Local neural network][Finite element input data]
- https://arxiv.org/abs/2402.01508[Deep learning path-like collective variable][Enhanced sampling molecular dynamics]
- https://arxiv.org/abs/2402.01578[Predictive model][Deep learning algorithm][Tensile deformation][AlCoCuCrFeNi high-entropy alloy]
- https://arxiv.org/abs/2405.18489[Predicting ground state properties][Constant sample complexity][Deep learning algorithm]
- https://arxiv.org/abs/2402.18830[Training-set-free two-stage deep learning][Spectroscopic data de-noising]
- https://arxiv.org/abs/2403.07163[Water isotope separation][Deep learning][Catalytically active ultrathin membrane]
- https://arxiv.org/abs/2403.12659[Zeolite adsorption property prediction][Deep learning]
- https://arxiv.org/abs/2403.13675[Bridging deep learning force field][Electronic structure][Physics-informed approach]
- https://arxiv.org/abs/2404.04810[AlphaCrystal-II][Distance matrix][Crystal structure prediction][Deep learning]
- https://arxiv.org/abs/2404.17130[Ultralow-power single-sensor-based E-nose system][Powered][Duty cycling][Deep learning][Real-time gas identification]
- https://arxiv.org/abs/2406.08191[Deep learning][Otical spectra][Semiconductors and insulators]
- https://arxiv.org/abs/2406.07761[Deep learning][Structural morphology][Imaged by scanning X-ray diffraction microscopy]
- https://arxiv.org/abs/2406.11134[Emergent Wigner phase][Moire superlattice][Deep learning]
- https://arxiv.org/abs/2406.17561[Improving density matrix electronic structure method][Deep learning]
- https://arxiv.org/abs/2407.00707[Deep learning quantum Monte Carlo][Solid]
- https://arxiv.org/abs/2407.12972[Deep learning][Quantitative dynamic fragmentation analysis]
- https://arxiv.org/abs/2407.13994[Evidential deep learning][Interatomic potential]
- https://arxiv.org/abs/2407.15214[Physical encoding][OOD Performance][Deep learning materials property prediction]
- https://arxiv.org/abs/2407.17290[Searching][New heavy fermion][Deep learning]
- https://arxiv.org/abs/2408.06237[Deep learning accelerated phase prediction][Refractory multi-principal element alloy]
- https://arxiv.org/abs/2408.06657[Physics informed deep learning][Strain gradient continuum plasticity]
- https://arxiv.org/abs/2408.13111[Time series forecasting][Multiphase microstructure evolution][Deep learning]
- https://arxiv.org/abs/2409.04603[Colloidoscope][Detecting dense colloid][3d with Deep Learning]
- https://arxiv.org/abs/2410.04765[Molecular topological deep learning][Polymer property prediction]
- https://arxiv.org/abs/1703.02309[Chemical composition alone][Predict Vibrational Free Energy][Entropies of Solids]
- https://arxiv.org/abs/2209.02362[Proper orthogonal descriptor][Efficient][Accurate interatomic potential]
- https://arxiv.org/abs/1612.04285[Learning physical descriptors][Materials science][Compressed sensing]
- https://arxiv.org/abs/2407.20273[Learning physics-consistent material behavior][Prior knowledge]
- https://arxiv.org/abs/2206.04117[Physics-guided descriptor][Prediction][Structural polymorph]
- https://arxiv.org/abs/2203.12353[Low-energy electron microscopy intensity-voltage data][Factorization][Sparse sampling][Classification]
- https://arxiv.org/abs/1807.10753[L0L1-norm compressive sensing paradigm][Construction of sparse predictive lattice model][Mixed integer quadratic programming]
- https://arxiv.org/abs/1911.04116[Sparse modeling][Quantum many-body Problem]
- https://arxiv.org/abs/2004.02426[Sparse modeling approach][Shear viscosity][Smeared correlation function]
- https://arxiv.org/abs/2007.03955[Sparse modeling][Large-scale quantum impurity model][Low symmetry]
- https://arxiv.org/abs/2106.12685[Efficient ab initio many-body calculation][Sparse modeling][Matsubara Green's function]
- https://arxiv.org/abs/2204.04475[Adaptive sparse sampling][Quasiparticle interference imaging]
- https://arxiv.org/abs/2205.14800[Sparse modeling approach][Quasiclassical theory][Superconductivity][J. Phys. Soc. Jpn. 92, 034703 (2023)]
- https://arxiv.org/abs/2206.04701[Efficient tensor network simulation][Quantum many-body physics][Sparse graph]
- https://arxiv.org/abs/2302.06844[Sparse random Fourier feature][Interatomic potential][High entropy alloy]
- https://arxiv.org/abs/1612.04307[Uncovering structure-property relationships of materials][Subgroup discovery]
- https://arxiv.org/abs/2312.13289[Stoichiometry representation learning][Polymorphic crystal structure]
- https://arxiv.org/abs/2205.15071[Incorporation][Density scaling constraint][Density functional design][Contrastive representation learning]
- https://arxiv.org/abs/2406.16853[GeoMFormer][General architecture][Geometric molecular representation learning]
- https://arxiv.org/abs/2408.01426[MolTRES][Improving chemical language representation learning][Molecular property prediction]
- https://arxiv.org/abs/2110.11444[Density of states prediction][Materials discovery][Contrastive learning][Probabilistic embedding]
- https://arxiv.org/abs/2205.05607[Simple framework][Contrastive learning][Phases of matter]
- https://arxiv.org/abs/2211.13408[Graph contrastive learning]
- https://arxiv.org/abs/1711.06387[Compositional descriptor-based recommender system][Accelerating the materials discovery]
- https://arxiv.org/abs/1710.00659[Matrix- and tensor-based recommender systems][Discovery of currently unknown inorganic compounds]
- https://arxiv.org/abs/2207.10747[Transferable recommender approach][Selecting][Best density functional approximation][Chemical discovery]
- https://arxiv.org/abs/1701.03966[Descriptor][Thermal expansion][Solid]
- https://arxiv.org/abs/1802.02957[Simple descriptor][Energetics][fcc-bcc metal interface]
- https://arxiv.org/abs/1805.08155[Physical descriptor][Gibbs energy][Inorganic crystalline solid][Prediction][Temperature-dependent materials chemistry]
- https://arxiv.org/abs/1809.04750[Important descriptors][descriptor groups][Curie temperature][Rare-earth transition-metal binary alloy]
- https://arxiv.org/abs/1810.03368[Electronic structure based descriptor][Characterizing][Local atomic environment]
- https://arxiv.org/abs/1811.07730[High-entropy][High-hardness][Metal carbide][Discovered][Entropy descriptor]
- https://arxiv.org/abs/2304.12880[Hardness descriptor][Derived][Symbolic regression]
- https://arxiv.org/abs/1905.06048[Materials property prediction][Symmetry-labeled graph][Atomic-position independent descriptor]
- https://arxiv.org/abs/1902.05867[Assessing correlation][Perovskite catalytic performance][Electronic structure descriptor]
- https://arxiv.org/abs/2009.06461[Maximum volume simplex method][Automatic selection][Classification][Atomic environment][Environment descriptor compression]
- https://arxiv.org/abs/2405.08137[LATTE][Atomic environment descriptor][Cartesian tensor contraction]
- https://arxiv.org/abs/2206.12129[Advancing descriptor search][Materials science][Feature engineering][Selection strategies]
- https://arxiv.org/abs/2301.06554[Descriptor-enabled rational design][High-entropy material][Over vast chemical space]
- https://arxiv.org/abs/2304.06282[High-performance descriptor][Magnetic material][Accurate discrimination][Magnetic symmetries]
- https://arxiv.org/abs/2305.02620[Ultra-high-density double-atom catalyst][Spin moment][Activity descriptor][Oxygen reduction reaction]
- https://arxiv.org/abs/2305.11617[Structural dynamics descriptor][Metal halide perovskite]
- https://arxiv.org/abs/2306.08316[Fast reconstruction][Microstructure][Ellipsoidal inclusion][Analytical descriptor]
- https://arxiv.org/abs/2307.00978[Neighbors Map][Efficient atomic descriptor][Structural analysis]
- https://arxiv.org/abs/2404.12853[Perspective on descriptors][Mechanical behavior][Cubic transition-metal carbides and nitrides]
- https://arxiv.org/abs/2008.08818[Ensemble learning][Dissimilarity][Rare-earth transition metal binary alloy][Curie temperature]
- https://arxiv.org/abs/2206.08011[Hardness prediction][Age-hardening aluminum alloy][Ensemble learning]
- https://arxiv.org/abs/2308.10818[Interpretable ensemble learning][Materials property prediction][Classical interatomic potential][Carbon as an example]
- https://arxiv.org/abs/2002.10649[Data analytics approach][Predict the hardness][Copper matrix composite]
- https://arxiv.org/abs/1906.01085[Unavoidable disorder][Entropy][Multi-component systems]
- https://arxiv.org/abs/1806.05772[Network analysis][Synthesizable materials discovery]
- https://arxiv.org/abs/1811.06060[Hybrid generative-discriminative model][Inverse materials design]
- https://arxiv.org/abs/2405.03680[AtomGPT][Atomistic generative pre-trained transformer][Forward and inverse materials design]
- https://arxiv.org/abs/2401.06779[VAE][Modified 1-hot generative materials modeling][Inverse material design]
- https://arxiv.org/abs/2409.06740[Data-efficient][Interpretable inverse materials design][Disentangled variational autoencoder]
- https://arxiv.org/abs/2106.10557[Learning][Delayed reward][Inverse defect design][2D material]
- https://arxiv.org/abs/2201.11591[Inversion][Chemical environment representation]
- https://arxiv.org/abs/2203.07157[Inverse Hamiltonian design][Automatic differentiation]
- https://arxiv.org/abs/2005.07609[Inverse design of crystal][Generalized invertible crystallographic representation]
- https://arxiv.org/abs/1908.07959[Inverse structural design][Graphene/Boron nitride hybrid][Regressional GAN]
- https://arxiv.org/abs/2005.04840[Inverse design][Lightweight broadband reflector][Efficient lightsail propulsion]
- https://arxiv.org/abs/1803.06061[Inverse design][Compact multi-wavelength waveguide][Cavity coupler]
- https://arxiv.org/abs/1904.10329[Classification scheme][Inverse design][Molecule][Targeted electronic properties][Atomicity]
- https://arxiv.org/abs/2004.01579[Inverse design][Ultralow lattice thermal conductivity][Lone pair cation coordination environment]
- https://arxiv.org/abs/2006.03413[Accelerating][Copolymer][Inverse design][AI Gaming algorithm]
- https://arxiv.org/abs/2007.07070[Inverse design][Graphene-based quantum transducer][Neuroevolution]
- https://arxiv.org/abs/2007.11228[CCDCGAN][Inverse design][Crystal structure]
- https://arxiv.org/abs/2008.02349[Inverse design][Surface][Deployable origami]
- https://arxiv.org/abs/2008.05298[Forward and inverse design][Kirigami][Supervised autoencoder]
- https://arxiv.org/abs/2008.10618[Inverse design][Composite metal oxide optical material][Deep transfer learning]
- https://arxiv.org/abs/2204.00433[Inverse design][Experimental verification][Bianisotropic metasurface][Optimization and machine learning]
- https://arxiv.org/abs/2104.08040[Inverse design][Crystal structure][Multicomponent system]
- https://arxiv.org/abs/2109.05484[Inverse design][Reconfigurable piezoelectric topological phononic plate]
- https://arxiv.org/abs/2112.00865[Inverse design][Strained graphene surface][Electron control]
- https://arxiv.org/abs/2106.03013[Inverse design][Two-dimensional material][Invertible neural networks]
- https://arxiv.org/abs/2104.06632[Inverse design][Glass structure][Deep graph neural network]
- https://arxiv.org/abs/2405.18891[Inverse design][Promising alloy][Electrocatalytic CO2 reduction][Generative graph neural network][Combined][Bird swarm algorithm]
- https://arxiv.org/abs/2205.07601[Volumetric-mapping-based inverse design][3D architected material][Mobility control][Topology reconstruction]
- https://arxiv.org/abs/2206.01043[Multi-objective inverse design][Solid-state quantum emitter][Single-photon source]
- https://arxiv.org/abs/2209.06608[Generation][Spin-wave pulse][Inverse design]
- https://arxiv.org/abs/2212.12106[Continuous action space tree search][Inverse design (CASTING) Framework][Materials discovery]
- https://arxiv.org/abs/2304.05422[Differentiable graph-structured model][Inverse design][Lattice material]
- https://arxiv.org/abs/2305.10020[Inverse design][All-dielectric metasurface][Bound state][Continuum]
- https://arxiv.org/abs/2304.08446[Inverse design][Next-generation superconductor][Data-driven deep generative model]
- https://arxiv.org/abs/2306.06585[Inverse design][Power-law nonlinear constitutive response][Stiffness normalization]
- https://arxiv.org/abs/2307.13581[Comparing forward][Inverse design paradigms][Case study][Refractory high-entropy alloy]
- https://arxiv.org/abs/2310.03537[Inverse design][Self-folding 3D shell][Phys. Rev. Lett. 132, 118201(2024)]
- https://arxiv.org/abs/2310.10995[Inverse design][Pyrochlore lattice][DNA origami][Model-driven experiment]
- https://arxiv.org/abs/2310.14775[Many-body quantum interference route][Two-channel Kondo effect][Inverse design][Molecular junction]
- https://arxiv.org/abs/2311.13328[MagGen][Graph aided deep generative model][Inverse design][Stable][Permanent magnet]
- https://arxiv.org/abs/2311.13812[Mechanical characterization][Inverse design][Stochastic architected metamaterial][Neural operator]
- https://arxiv.org/abs/2312.03690[Inverse design][Vitrimeric polymer][Molecular dynamics][Generative modeling]
- https://arxiv.org/abs/2402.09031[Inverse design][Casimir-Lifshitz force][Heterogeneous gapped metal surface]
- https://arxiv.org/abs/2402.13054[Inverse design][Spinodoid structure][Bayesian optimization]
- https://arxiv.org/abs/2402.11600[AI-assisted inverse design][Sequence-ordered high intrinsic thermal conductivity polymer]
- https://arxiv.org/abs/2403.15887[Tutorial][AI-assisted exploration][Active design][Polymer][High intrinsic thermal conductivity]
- https://arxiv.org/abs/2402.16452[Multiscale experiment][Predictive modelling][Inverse design][Failure mitigation][Additively manufactured lattice]
- https://arxiv.org/abs/2403.15725[Customizable wave tailoring material][Coupling nonlinear inverse design][Two scales]
- https://arxiv.org/abs/2406.09263[Generative inverse design][Crystal structure][Diffusion model][Transformer]
- https://arxiv.org/abs/2406.10566[Inverse design][Programmable shape-morphing kirigami structure]
- https://arxiv.org/abs/2407.00729[Discovering one molecule][Out of a million][Inverse design][Molecular hole transporting semiconductor][Perovskite solar cell]
- https://arxiv.org/abs/2407.10273[Quantized inverse design][Photonic integrated circuit]
- https://arxiv.org/abs/2407.16736[Inverse design][Polaritonic device]
- https://arxiv.org/abs/2408.06300[Inverse designing metamaterial][Programmable nonlinear functional responses][Graph space]
- https://arxiv.org/abs/2408.07608[MatterGPT][Generative transformer][Multi-property inverse design][Solid-state material]
- https://arxiv.org/abs/2409.03275[Inverse design][Winding tuple][Non-Hermitian topological edge mode]
- https://arxiv.org/abs/2409.18284[Inverse design][Unitary transmission matrices][Silicon photonic coupled waveguide array][Neural adjoint model]
- https://arxiv.org/abs/2410.05833[dCG][Differentiable connected geometries][Multi-domain optimization][Inverse design]
- https://arxiv.org/abs/2202.06699[Inverse-designed metaphotonics][Hypersensitive detection]
- https://arxiv.org/abs/2403.17724[Magnonic inverse-design processor]
- https://arxiv.org/abs/2409.16572[Efficient and generalizable nested Fourier-DeepONet][Three-dimensional geological carbon sequestration]
- https://arxiv.org/abs/2203.04491[Towards large-scale][Spatio-temporally resolved diagnosis][Electronic density of states][DNN]
- https://arxiv.org/abs/2408.03198[Coercivity influence][Nanostructure][SmCo-1:7 magnet][Machine learning][High-throughput micromagnetic data]
- https://arxiv.org/abs/2408.17023[Physics-integrated neural network][Quantum transport prediction][Field-effect transistor]
- https://arxiv.org/abs/1809.03555[Deep neural network][Inverse design][Integrated nanophotonic device]
- https://arxiv.org/abs/2201.10387[Deep neural network][Prediction][Optical properties][Free-form inverse design][Metamaterial]
- https://arxiv.org/abs/2204.10430[Simple denoising approach][Exploit multi-fidelity data][Machine learning materials properties]
- https://arxiv.org/abs/2302.02303[Inorganic synthesis recommendation][Machine learning materials similarity][Scientific literature]
- https://arxiv.org/abs/2406.15650[Machine learning materials properties][Accurate prediction][Uncertainty estimate][Domain guidance][Persistent online accessibility]
- https://arxiv.org/abs/1901.00081[Machine learning materials physics][Integrable deep neural network][Scale bridging][Learning free energy function]
- https://arxiv.org/abs/1901.00524[Machine learning materials physics][Deep neural network][Trained][Elastic free energy data][Martensitic microstructure][Predict homogenized stress field][High accuracy]
- https://arxiv.org/abs/2311.14972[Leveraging neural network][Attention mechanism][High-order accuracy][Charge density][Particle-in-cell simulation]
- https://arxiv.org/abs/2010.04905[Accelerating finite-temperature Kohn-Sham density functional theory][Deep neural network]
- https://arxiv.org/abs/1811.08425[Fast classification][Small X-ray diffraction dataset][Data augmentation][Deep neural network]
- https://arxiv.org/abs/1902.02528[In-Memory][Error-Immune][Differential RRAM implementation][Binarized Deep Neural Network]
- https://arxiv.org/abs/1906.11434[Deep neural network][Wannier function center]
- https://arxiv.org/abs/1909.05529[Deep neural network][X-ray photoelectron spectroscopy][Data analysis]
- https://arxiv.org/abs/1910.00252[Coarse-grained deep neural network model][Liquid water]
- https://arxiv.org/abs/1912.05044[Unified deep neural network][Potential capable][Predicting thermal conductivity][Silicon in different phases]
- https://arxiv.org/abs/2004.07369[Raman spectrum][Polarizability][Liquid water][Deep neural network]
- https://arxiv.org/abs/2004.08247[Real-time sparse-sampled Ptychographic imaging][Deep neural network]
- https://arxiv.org/abs/2010.1353[Learning hidden elasticity][Deep neural network]
- https://arxiv.org/abs/2012.03019[Deep neural network][Ppredict][Quantum many-body Hamiltonian][Learning visualized wave-function]
- https://arxiv.org/abs/2101.07770[Developing][Deep neural network][Denoise time-resolved][In situ][ETEM][Catalyst nanoparticle]
- https://arxiv.org/abs/2101.11902[Learning hidden chemistry][Deep neural network]
- https://arxiv.org/abs/2103.14174[Copolymer informatics][Multi-task deep neural network]
- https://arxiv.org/abs/2104.03786[Deep neural network representation][Density functional theory Hamiltonian]
- https://arxiv.org/abs/2105.01384[Deep neural network][Predictive-generative framework][Designing composite materials]
- https://arxiv.org/abs/2106.03126[Predicting quantum potential][Deep neural network][Metropolis sampling]
- https://arxiv.org/abs/2111.05603[Pairwise interaction][Potential energy surface][Atomic force][Deep neural network]
- https://arxiv.org/abs/2111.07284[Energy efficient learning][Low resolution stochastic domain wall synapse][Deep neural network]
- https://arxiv.org/abs/2212.06084[Hardware-efficient learning][Quantum many-body state]
- https://arxiv.org/abs/2403.14131[Efficient learning strategy][Predicting glass forming ability][Imbalanced dataset][Bulk metallic glasses]
- https://arxiv.org/abs/2111.13956[Understanding anharmonic effect][Hydrogen desorption characteristics][MgnH2n nanocluster][Ab initio trained deep neural network]
- https://arxiv.org/abs/2112.06142[Semi-supervised teacher-student][Deep neural network][materials discovery]
- https://arxiv.org/abs/2203.12033[Bioplastic design][Multitask deep neural network]
- https://arxiv.org/abs/2204.13912[Quantitative prediction][Fracture toughness (KIc)][Polymer][Fractography][Deep neural network]
- https://arxiv.org/abs/2208.00349[Deep neural network][Disordered Structure][Glasses?]
- https://arxiv.org/abs/2211.07749[High-accuracy variational Monte Carlo][Frustrated magnet][Deep neural network]
- https://arxiv.org/abs/2211.08482[DyFraNet][Forecasting][Backcasting][Dynamic fracture mechanics][Space and time using a 2D-to-3D deep neural network]
- https://arxiv.org/abs/2212.13678[HubbardNet][Efficient prediction][Bose-Hubbard model spectrum][Deep neural network]
- https://arxiv.org/abs/2302.08965[A simple method][Multi-body wave function][Ground and low-lying excited states][Deep neural network]
- https://arxiv.org/abs/2302.12030[Back-end][Flexible substrate compatible analog ferroelectric field effect transistor][Accurate online training][Deep neural network accelerator]
- https://arxiv.org/abs/2303.15538[GlassNet][Multitask deep neural network][Predicting many glass properties]
- https://arxiv.org/abs/2304.12400[Generative discovery][Novel chemical design][Diffusion modeling][Transformer][Deep neural network][Application][Deep eutectic solvent]
- https://arxiv.org/abs/2305.02954[Quantifying][Magnetic interaction][Governing][Chiral spin texture][Deep neural network]
- https://arxiv.org/abs/2311.16135[Deep neural network][Uncertain stress function][Extension][Impact mechanics]
- https://arxiv.org/abs/2311.18686[Highly efficient][Transferable interatomic potential][alpha-iron][alpha-iron/hydrogen binary system][Deep neural network]
- https://arxiv.org/abs/2405.15502[Unsupervised deep neural network approach][To solve Fermionic systems]
- https://arxiv.org/abs/2405.15488[Unsupervised deep neural network approach][To Solve Bosonic systems]
- https://arxiv.org/abs/2406.01222[Symmetry enforced solution][Many-body Schrödinger equation][Deep neural network]
- https://arxiv.org/abs/2407.00294[Deep neural network][Symplectic preservation properties]
- https://arxiv.org/abs/2407.12293[Multi evolutional deep neural network][Multi-EDNN]
- https://arxiv.org/abs/2409.02339[Data-driven 2D stationary quantum droplet][Wave propagation][Amended GP equation][Two potentials][Deep neural networks learning]
- https://arxiv.org/abs/2209.09247[Weak-signal extraction][Enabled][Deep-neural-network denoising][Diffraction data]
- https://arxiv.org/abs/2203.01262[Viscosity][Water][First-principles][Deep-neural-network simulation]
- https://arxiv.org/abs/2303.18055[Deep neural operator][Learning transient response][Interpenetrating phase composite][Subject to dynamic loading]
- https://arxiv.org/abs/2111.05885[Predicting lattice phonon vibrational frequencies][Deep graph neural network]
- https://arxiv.org/abs/2010.05851[Graph neural network][Accurate][Interpretable prediction][Polycrystalline material]
- https://arxiv.org/abs/2010.159[Graph neural network][Metal organic framework][Potential energy approximation]
- https://arxiv.org/abs/2102.11023[Predicting material properties][3D Graph neural network][Invariant local descriptor]
- https://arxiv.org/abs/2106.01829[Atomistic line graph neural network][Improved materials property prediction]
- https://arxiv.org/abs/2107.05142[Graph neural network][Emulating crack coalescence][Propagation][Brittle material]
- https://arxiv.org/abs/2109.12283[Scalable deeper graph neural network][High-performance materials property prediction]
- https://arxiv.org/abs/2109.14012[Atomistic graph neural network][Application][bcc iron]
- https://arxiv.org/abs/2111.05557[Piezoelectric modulus prediction][Machine learning][Graph neural network]
- https://arxiv.org/abs/2108.02077[Entropy-based active learning][Graph neural network surrogate model][Materials Properties]
- https://arxiv.org/abs/2111.14712[Prediction][Large magnetic moment][Graph neural network][Random forest]
- https://arxiv.org/abs/2409.19209[Boosting SISSO performance][Small sample dataset][Random forests prescreening][Complex feature selection]
- https://arxiv.org/abs/2112.10231[Graph neural network prediction][Metal organic framework][CO2 Adsorption]
- https://arxiv.org/abs/2201.05770[Edge-based tensor prediction][Graph neural network]
- https://arxiv.org/abs/2201.08348[Prediction][Electron density of states][Crystalline compound][Atomistic line graph neural network][ALIGNN]
- https://arxiv.org/abs/2202.01954[Multi-task graph neural network][Simultaneous prediction][Global and atomic properties][Ferromagnetic system]
- https://arxiv.org/abs/2203.10177[Characterizing disorder][Atomic environment][Physics-preserving graph neural network]
- https://arxiv.org/abs/2205.05475[Efficient prediction][Density functional theory Hamiltonian][Graph neural network]
- https://arxiv.org/abs/2205.06324[Graph neural network modeling][Grain-scale anisotropic elastic behavior][Simulated][Measured][Microscale data]
- https://arxiv.org/abs/2206.12867[Edge direction-invariant][Graph neural network][Molecular dipole moments prediction]
- https://arxiv.org/abs/2207.12510[Rapid prediction][Phonon structure][Atomistic line graph neural network][ALIGNN]
- https://arxiv.org/abs/2208.03296[Accelerating discrete dislocation dynamics simulation][Graph neural network]
- https://arxiv.org/abs/2208.05039[Examining graph neural network][Crystal structure][Limitation][Opportunities][Capturing periodicity]
- https://arxiv.org/abs/2208.09481[Graph neural network][Materials science and chemistry]
- https://arxiv.org/abs/2208.14364[Dynamic][Adaptive mesh-based graph neural network framework][Simulating displacement][Crack field][Phase field model]
- https://arxiv.org/abs/2209.05554[Unified graph neural network force-field][Periodic table]
- https://arxiv.org/abs/2209.05583[Polycrystal graph neural network]
- https://arxiv.org/abs/2209.07300[Multi-task mixture density graph neural network][Predicting][Cu-based single-atom alloy catalyst][CO2 reduction reaction]
- https://arxiv.org/abs/2209.07567[Prediction][CO2 adsorption][Nano-pore][Graph neural network]
- https://arxiv.org/abs/2209.13557[Polymer informatics][At-scale][Multitask graph neural network]
- https://arxiv.org/abs/2211.03563[Self-supervised representation][Node embedding graph neural network][Accurate][Multi-scale analysis]
- https://arxiv.org/abs/2212.10948[Prediction][Steel nanohardness][Graph neural network][Surface polycrystallinity map]
- https://arxiv.org/abs/2301.12059[Potential energy surface prediction][Alumina polymorph][Graph neural network]
- https://arxiv.org/abs/2101.03164[SE(3)-equivariant graph neural network][Data-efficient and accurate interatomic potential]
- https://arxiv.org/abs/2306.12818[StrainNet][Predicting crystal structure elastic properties][SE(3)-equivariant graph neural network]
- https://arxiv.org/abs/2403.11347[Phonon prediction][E(3)-equivariant graph neural network]
- https://arxiv.org/abs/2306.14238[GPT-assisted learning][Structure-property relationship][Graph neural network][Application][Rare-earth doped phosphor]
- https://arxiv.org/abs/2307.15242[Universal equivariant graph neural network][Elasticity tensor][Any crystal system]
- https://arxiv.org/abs/2310.02428[EGraFFBench][Evaluation][Equivariant graph neural network force field][Atomistic simulation]
- https://arxiv.org/abs/2409.09931[Generalizability][Graph neural network force field][Predicting solid-state properties]
- https://arxiv.org/abs/2405.07915[Discovery][Highly anisotropic dielectric crystal][Equivariant graph neural network]
- https://arxiv.org/abs/2406.03563[Equivariant graph neural network][Prediction][Tensor material properties][Crystal]
- https://arxiv.org/abs/2307.03907[Relationship][Activated H2 bond length][Adsorption distance][MXene][Identified][Graph neural network][Resonating valence bond theory]
- https://arxiv.org/abs/2307.05299[Discovering symbolic laws directly][Trajectories][Hamiltonian graph neural network]
- https://arxiv.org/abs/2308.11160[Predicting transition temperature][Superconductor][Graph neural network]
- https://arxiv.org/abs/2309.04811[Chemical Properties][Graph neural network-predicted electron densities]
- https://arxiv.org/abs/2410.13768[Rapid and automated alloy design][Graph neural network-powered LLM-driven multi-agent system]
- https://arxiv.org/abs/2309.06423[Accelerating defect prediction][Semiconductor][Graph neural network]
- https://arxiv.org/abs/2310.06995[Accelerated modelling][Interface][Electronic device][Graph neural network]
- https://arxiv.org/abs/2310.15153[Accelerate microstructure evolution simulation][Graph neural network][Adaptive spatiotemporal resolution]
- https://arxiv.org/abs/2310.19274[Prediction][Effective elastic moduli][Rocks using graph neural network]
- https://arxiv.org/abs/2310.19500[Coarse-grained crystal graph neural network][Reticular materials design]
- https://arxiv.org/abs/2311.00939[Accelerated data-driven discovery][Screening][Two-dimensional magnet][Graph neural network]
- https://arxiv.org/abs/2311.02143[Pairing-based graph neural network][Simulating quantum material]
- https://arxiv.org/abs/2402.18379[Embracing disorder][Quantum materials design]
- https://arxiv.org/abs/2311.07548[Interpretable fine-tuning][Graph neural network surrogate model]
- https://arxiv.org/abs/2403.07795[Fine-tuning neural network][Quantum state]
- https://arxiv.org/abs/2401.11768[ADA-GNN][Atom-distance-angle graph neural network][Crystal material property prediction]
- https://arxiv.org/abs/2401.16271[AnisoGNN][Graph neural network][Generalizing][Anisotropic properties][Polycrystal]
- https://arxiv.org/abs/2401.16565[Towards accurate prediction][Configurational disorder properties][Material][Graph neural network]
- https://arxiv.org/abs/2403.15266[Graph neural network coarse-grain force field][Molecular crystal RDX]
- https://arxiv.org/abs/2404.08782[Phase transition][Correlated system][Graph neural network][Quantum embedding techniques]
- https://arxiv.org/abs/2405.02078[CatTSunami][Accelerating transition state energy calculation][Pre-trained graph neural network]
- https://arxiv.org/abs/2405.00814[Solving Maxwell's equation][Non-trainable graph neural network message passing]
- https://arxiv.org/abs/2405.05205[Hybrid quantum graph neural network][Molecular property prediction]
- https://arxiv.org/abs/2405.08628[Chemical-motif characterization][Short-range order][E(3)-equivariant graph neural network]
- https://arxiv.org/abs/2405.11502[CTGNN][Crystal transformer graph neural network][Crystal material property prediction]
- https://arxiv.org/abs/2405.16511[SE3Set][Harnessing equivariant hypergraph neural network][Molecular representation learning]
- https://arxiv.org/abs/2406.08682[FIP-GNN][Graph neural network][Scalable prediction][Grain-level fatigue indicator parameter]
- https://arxiv.org/abs/2406.16654[Ensemble-embedding graph neural network][Direct prediction][Optical spectra][Crystal structure]
- https://arxiv.org/abs/2407.05204[Leveraging persistent homology feature][Accurate defect formation energy prediction][Graph neural network]
- https://arxiv.org/abs/2407.10458[Predicting doping strategies][Ternary nickel-cobalt-manganese cathode material][Enhance battery performance][Graph neural network]
- https://arxiv.org/abs/2407.10844[Rotationally invariant latent distance][Uncertainty estimation][Relaxed energy prediction][Graph neural network potential]
- https://arxiv.org/abs/2410.01650[Accessing numerical energy Hessians][Graph neural network potential][Their application][Heterogeneous catalysis]
- https://arxiv.org/abs/2408.16337[Graph neural network][Work][High entropy alloy]
- https://arxiv.org/abs/2408.16698[SympGNNs][Symplectic graph neural network][Identifiying high-dimensional Hamiltonian system][Node classification]
- https://arxiv.org/abs/2409.05306[Investigating material interface diffusion phenomena][Graph neural network][Applied material]
- https://arxiv.org/abs/2409.07339[Descriptors-free collective variable][Geometric graph neural network]
- https://arxiv.org/abs/2409.07664[Rapid assessment][Stable crystal structure][Single phase high entropy alloy][Graph neural network][Surrogate modelling]
- https://arxiv.org/abs/2409.13851[Learning ordering][Crystalline material][Symmetry-aware graph neural network]
- https://arxiv.org/abs/2409.15800[MGNN][Moment graph neural network][Universal molecular potential]
- https://arxiv.org/abs/2410.01657[Scalable and consistent graph neural network][Distributed mesh-based data-driven modeling]
- https://arxiv.org/abs/1702.05771[Predicting electronic structure properties][Transition metal complexes][Neural Network]
- https://arxiv.org/abs/1803.03039[Design][Nickel-base superalloy][Neural network]
- https://arxiv.org/abs/2011.07929[Equivalence][Molecular graph convolution][Molecular wave function][Poor basis set]
- https://arxiv.org/abs/2303.04791[Ewald-based long-range message passing][Molecular graph]
- https://arxiv.org/abs/2312.16473[MolSets][Molecular graph deep sets learning][Mixture property modeling]
- https://arxiv.org/abs/2307.05392[Simplicial message passing][Chemical property prediction]
- https://arxiv.org/abs/1807.04955[Self-learning Monte Carlo method][Behler-Parrinello neural network]
- https://arxiv.org/abs/2306.11527[Self-learning Monte Carlo][Equivariant transformer]
- https://arxiv.org/abs/1905.10407[De novo exploration][Self-guided learning][Potential-energy surface]
- https://arxiv.org/abs/2301.11543[Machine-guided design][Oxidation resistant superconductor][Quantum information application]
- https://arxiv.org/abs/1808.01696[Physically-informed artificial][Neural network][Atomistic modeling]
- https://arxiv.org/abs/1809.08221[Simulated Bloch oscillation][Strained graphene][Neural network]
- https://arxiv.org/abs/2207.11096[Seeing moire][Convolutional network learning][Twistronics]
- https://arxiv.org/abs/2403.19214[Convolutional network learning][Self-consistent electron density][Grid-projected atomic fingerprint]
- https://arxiv.org/abs/1910.11516[Leveraging legacy data][Accelerate materials design][Preference learning]
- https://arxiv.org/abs/2305.01101[Leveraging language representation][Material recommendation][Ranking][Exploration]
- https://arxiv.org/abs/1807.09422[Solving frustrated quantum many-particle model][Convolutional neural network]
- https://arxiv.org/abs/2010.09485[Determination][Interface][Amorphous insulator][Crystalline 4H-SiC][Transmission electron microscope image][Convolutional neural network]
- T. Xie and J. C. Grossman, Phys. Rev. Lett. 120, 145301(2018)[Crystal graph convolutional][Neural network][Accurate][Interpretable][Prediction material properties]
- https://arxiv.org/abs/1807.03404[Hierarchical visualization][Materials space][Graph convolutional][Neural network]
- https://arxiv.org/abs/1810.06118[Learning to fail][Predicting fracture evolution][Brittle material][Recurrent graph convolutional neural network]
- https://arxiv.org/abs/1811.05660[MT-CGCNN][Integrating crystal graph convolutional neural network][Multitask learning][Material property prediction]
- https://arxiv.org/abs/2008.06415[Orbital graph convolutional neural network][Material property prediction]
- https://arxiv.org/abs/1906.05267[Developing][Improved crystal graph convolutional neural network framework][Accelerated materials discovery]
- https://arxiv.org/abs/2001.10908[Super resolution convolutional neural network][Feature extraction][Spectroscopic data]
- https://arxiv.org/abs/1811.06231[Graph convolutional Neural Network][Polymers Property Prediction]
- https://arxiv.org/abs/2003.08749[Reliable quality monitoring system][Additive manufacturing process][Deep convolutional neural network]
- https://arxiv.org/abs/2003.13425[Predicting elastic property][Electronic charge density][3D deep convolutional neural network]
- https://arxiv.org/abs/2107.04287[Iterative peak-fitting][Frequency-domain data][Deep convolution neural network]
- https://arxiv.org/abs/2003.13379[Global attention][Graph convolutional neural network][Improved materials property prediction]
- https://arxiv.org/abs/2006.15895[Ionic surfactant][Nafion][Convolutional neural network]
- https://arxiv.org/abs/2007.09932[Transfer learning][Materials informatics][Crystal graph convolutional neural network]
- https://arxiv.org/abs/2007.14144[Temperature-transferable coarse-graining][Ionic liquid][Dual graph convolutional neural network]
- https://arxiv.org/abs/2404.17584[Equivariant graph convolutional neural network][Representation][Homogenized anisotropic microstructural mechanical response]
- https://arxiv.org/abs/2402.11179[Uncertainty quantification][Graph convolution neural network model][Evolving Processes]
- https://arxiv.org/abs/2409.08940[Representing Born effective charge][Equivariant graph convolutional neural network]
- https://arxiv.org/abs/2410.11178[Combining reinforcement learning][Graph convolutional neural network][Efficient design][TiAl/TiAlN atomic-scale interface]
- https://arxiv.org/abs/1812.02949[Slab gaph convolutional neural ntwork][Discovery][N2 Electroreduction Catalyst]
- https://arxiv.org/abs/2009.14370[Hybrid convolutional neural network][PEPS wave function][Quantum many-particle state]
- https://arxiv.org/abs/2010.03675[Predicting][Mechanical Property][Microstructure image][Fiber-reinforced polymer][Convolutional neural network]
- https://arxiv.org/abs/2011.03474[Correlator convolutional neural network][Interpretable architecture][Image-like quantum matter data]
- https://arxiv.org/abs/2407.21502[Interpretable correlator transformer][Image-like quantum matter data]
- https://arxiv.org/abs/2011.12911[Learning crystal field parameter][Convolutional neural network]
- https://arxiv.org/abs/2103.00001[3D coherent x-ray imaging][Deep convolutional neural network]
- https://arxiv.org/abs/2103.02638[On-lattice voxelated convolutional neural network][Prediction][Phase diagram][Diffusion barrier][Cubic alloy]
- https://arxiv.org/abs/2106.16179[Convolutional neural network][Defect classification][Bragg coherent X-ray diffraction]
- https://arxiv.org/abs/2109.03020[Deep convolutional neural network][Predict][Elasticity tensor][Homogenization]
- https://arxiv.org/abs/2303.17025[Deep convolutional neural network][Restore][Single-shot electron microscopy image]
- https://arxiv.org/abs/2102.03877[Noise reduction][X-ray photon correlation spectroscopy][Convolutional neural network][Encoder-decoder model]
- https://arxiv.org/abs/2201.09672[DH-Net][Deformed microstructure homogenization][3D convolutional neural network]
- https://arxiv.org/abs/2203.06503[Convolutional neural network][Enable high-fidelity prediction][Path-dependent diffusion barrier spectra][Multi-principal element alloy]
- https://arxiv.org/abs/2205.01167[3D convolutional neural network][Dendrite segmentation][Fine-tuning][Hyperparameter optimization]
- https://arxiv.org/abs/2207.14603[Orthogonal spin current][Injected magnetic tunnel junction][Convolutional neural network]
- https://arxiv.org/abs/2210.00692[Interpreting convolutional neural networks' low dimensional approximation][Quantum spin system]
- https://arxiv.org/abs/2211.12596[Application][Convolutional neural network][TSOM Image][Classification][6 nm node patterned defect]
- https://arxiv.org/abs/1810.03787[Quantum convolutional][Neural network]
- https://arxiv.org/abs/2211.11786[Model-independent learning][Quantum phases of matter][Quantum convolutional neural network]
- https://arxiv.org/abs/2407.04114[Quantum convolutional neural network][Phase recognition][Two dimension]
- https://arxiv.org/abs/2212.02805[Interdisciplinary discovery][Nanomaterial][Convolutional neural network]
- https://arxiv.org/abs/2302.01390[Controlling][Skyrmion density and size][Quantized convolutional neural network]
- https://arxiv.org/abs/2306.11833[Convolutional neural network][Large-scale dynamical modeling][Itinerant magnet]
- https://arxiv.org/abs/2306.12874[Charting nanocluster structure][Convolutional neural network]
- https://arxiv.org/abs/2310.18035[High throughput screening][Ternary nitride][Convolutional neural network]
- https://arxiv.org/abs/2310.19128[Prediction][Local elasto-plastic stress][Strain field][Two-phase composite microstructure][Deep convolutional neural network]
- https://arxiv.org/abs/2312.09223[Many-body mobility edge][1D and 2D revealed][Convolutional neural network]
- https://arxiv.org/abs/2402.03876[Convolutional neural network][Volcano plot][Screening][Prediction][Two-dimensional single-atom catalyst]
- https://arxiv.org/abs/2404.05901[Quantum-inspired activation function][Convolutional neural network]
- https://arxiv.org/abs/2405.03049[Quantitative analysis][Prediction performance][Convolutional neural network][Evaluating][Surface elastic energy][Strained film]
- https://arxiv.org/abs/2405.05156[Crystal structure identification][3D convolutional neural network][Application][High-pressure phase transition][SiO2]
- https://arxiv.org/abs/2405.17696[Physics-guided full waveform inversion][Encoder-solver convolutional neural network]
- https://arxiv.org/abs/2204.07234[Physics-aware recurrent convolutional][PARC][Neural network][Assimilate meso-scale][Reactive mechanics][Energetic material]
- https://arxiv.org/abs/2009.09684[Determining electronic properties][L-edge X-ray absorption spectra][Transition metal compound][Artificial neural network]
- https://arxiv.org/abs/1803.00133[Materials data validation][Imputation][Artificial neural network]
- https://arxiv.org/abs/1806.05773[Notes on derivation][Streamline field][Artificial neural network][Automatic simulation][Material forming process]
- https://arxiv.org/abs/1807.09527[Adiabatic superconducting][Artificial neural network][Basic cell]
- https://arxiv.org/abs/1808.02069[Generalized transfer matrix state][Artificial neural network]
- https://arxiv.org/abs/1809.05519[Identifying quantum phase transition][Artificial neural network][Experimental data]
- https://arxiv.org/abs/1902.07443[On-the-fly adaptivity][Nonlinear twoscale simulation][Artificial neural network][Reduced order modeling]
- https://arxiv.org/abs/1811.03774[Artificial neural network][Density-functional optimization][Fermionic system]
- https://arxiv.org/abs/1812.04394[Gap prediction][Hybrid graphene][Hexagonal boron nitride][Nanoflake][Artificial neural network]
- https://arxiv.org/abs/1905.07440[Accelerating lattice quantum Monte Carlo simulation][Artificial neural network][Application to the Holstein model]
- https://arxiv.org/abs/2004.10167[Grain boundary slip transfer classification][Metric selection][Artificial neural network]
- https://arxiv.org/abs/2005.02789[Predicting][Porosity][Freeze casting][Artificial neural network]
- https://arxiv.org/abs/2009.00019[Solving the Liouvillian gap][Artificial neural network][Phys. Rev. Lett. 126, 160401(2021)]
- https://arxiv.org/abs/2009.04473[Unitary long-time evolution][Quantum renormalization group][Artificial neural network]
- https://arxiv.org/abs/2009.09684[Determining electronic properties][L-edge X-ray absorption spectra][Transition metal compound][Artificial neural network]
- https://arxiv.org/abs/2011.14505[Atomistic mechanism][Underlying the Si(111)-(7x7) surface reconstruction][Revealed][Artificial neural-network potential]
- https://arxiv.org/abs/2012.11586[Analyzing non-equilibrium quantum state][Snapshot][Artificial neural network][Phys. Rev. Lett. 127, 150504 (2021)]
- https://arxiv.org/abs/2012.11137[Structural phase transition][Two dimensional single-layer SnTe][Artificial neural network]
- https://arxiv.org/abs/2102.02345[LAMMPS implementation][Rapid artificial neural network][Interatomic potential]
- https://arxiv.org/abs/2103.03367[Training artificial neural network][Precision orientation][Strain mapping][4D electron diffraction dataset]
- https://arxiv.org/abs/2104.00013[Time-dependent variational principle][Open quantum system][Artificial neural network][Phys. Rev. Lett. 127, 230501 (2021)]
- https://arxiv.org/abs/2105.15193[Artificial neural network state][Non-additive system]
- https://arxiv.org/abs/2106.12745[Artificial neural network molecular mechanics][Iron grain boundary]
- https://arxiv.org/abs/2108.13137[Thermodynamics-based artificial neural networks][TANN][Multiscale modeling][Inelastic microstructure]
- https://arxiv.org/abs/2110.00724[Complex spin Hamiltonian][Artificial neural network]
- https://arxiv.org/abs/2110.09815[Microstructure reconstruction][Artificial neural network][Combination of causal and non-causal approach]
- https://arxiv.org/abs/2204.11126[Near-real-time diagnosis][Electron optical phase aberration][Scanning transmission electron microscopy][Artificial neural network]
- https://arxiv.org/abs/2205.08378[Machine learning][Atomic layer deposition][Predicting][Saturation time][Reactor growth profile][Artificial neural network]
- https://arxiv.org/abs/2206.00166[Spectral recognition][Magnetic nanoparticle][Artificial neural network]
- https://arxiv.org/abs/2206.02388[Extracting electronic many-body correlation][Local measurement][Artificial neural network]
- https://arxiv.org/abs/2209.06001[Integrated artificial neural network][Trainable activation function][Enabled][Topological insulator-based spin-orbit torque device]
- https://arxiv.org/abs/2210.00365[Efficient solution][Fermionic System][Artificial neural network]
- https://arxiv.org/abs/2210.02202[New family][Constitutive artificial neural network][Automated model discovery]
- https://arxiv.org/abs/2210.16994[Comparison][Two artificial neural networks][Trained][Surrogate modeling][Stress][Materially heterogeneous elastoplastic solid]
- https://arxiv.org/abs/2211.01779[Exploring explicit coarse-grained structure][Artificial neural network]
- https://arxiv.org/abs/2211.04796[Physics-separating artificial neural network][Predicting initial stage][Al sputtering][Thin film deposition][Ar plasma discharge]
- https://arxiv.org/abs/2301.01521[Artificial neural network][Effective tool][Positron annihilation lifetime spectra]
- https://arxiv.org/abs/2301.03524[Physics-separating artificial neural network][Predicting sputtering and thin film deposition][AlN][Ar/N2][Discharge][Experimental timescale]
- https://arxiv.org/abs/2301.12262[Arrhenius crossover temperature][Glass-forming liquid][Predicted][Artificial Neural Network]
- https://arxiv.org/abs/2303.12164[Viscoelastic constitutive artificial neural networks (vCANNs)][Framework][Data-driven anisotropic nonlinear finite viscoelasticity]
- https://arxiv.org/abs/2304.07899[Artificial neural network-based density functional approach][Adiabatic energy difference][Transition metal complexes]
- https://arxiv.org/abs/2404.15658[Neural network-based recognition][Multiple nanobubble][Graphene]
- https://arxiv.org/abs/2410.09177[From Ferminet to PINN][Connection][Neural network-based algorithm][High-dimensional Schrödinger Hamiltonian]
- https://arxiv.org/abs/2307.10139[Artificial neural network][Predicting mechanical properties][Crystalline polyamide12][Molecular dynamics simulation]
- https://arxiv.org/abs/2310.20398[Hybrid approach][Solving the gravitational N-body problem][Artificial neural network][Journal of Computational Physics, Volume 496, 112596 (2024)]
- https://arxiv.org/abs/2311.06380[Theory][Implementation][Inelastic constitutive artificial neural network]
- https://arxiv.org/abs/2401.17116[Quantum error mitigation][Correction][Mediated by Yang-Baxter equation][Artificial neural network]
- https://arxiv.org/abs/2410.02633[Quantum many-body solver][Artificial neural network][Application][Strongly correlated electron system]
- https://arxiv.org/abs/1812.09329[QuCumber][Wavefunction reconstruction][Neural network]
- https://arxiv.org/abs/1901.00032[Inorganic materials synthesis planning][Literature-trained neural network]
- https://arxiv.org/abs/1902.01845[Probing topological property][3D lattice dimer model][Neural network]
- https://arxiv.org/abs/2207.09056[Learning quantum dissipation][Neural ordinary differential equation]
- https://arxiv.org/abs/1902.09483[Variational quantum Monte Carlo][Neural network ansatz][Open quantum system]
- https://arxiv.org/abs/2203.15472[Ab initio calculation][Real solid][Neural network ansatz]
- https://arxiv.org/abs/1903.09101[Scanning probe state recognition][Multi-class neural network ensembles]
- https://arxiv.org/abs/1904.13165[Neural network setup][Precise detection][Many-body localization transition][Finite-size scaling][limitation]
- https://arxiv.org/abs/1905.01536[Neural network][Path collective variable][Enhanced sampling of phase transformation]
- https://arxiv.org/abs/1905.06371[Roadmap on material-function mapping][Photonic-electronic hybrid neural network]
- https://arxiv.org/abs/1905.09168[Predicting quantum many-body dynamics][Long short-term memory][Neural network]
- https://arxiv.org/abs/1910.02898[Fast fitting of reflectivity data][Growing thin film][Neural network]
- https://arxiv.org/abs/1910.03172[Prediction][Evolution of the stress field][Polycrystal][Elastic-plastic deformation][Hybrid neural network model]
- https://arxiv.org/abs/1910.12919[Domain wall synapse][Non volatile memory device][On-chip learning][Analog hardware neural network]
- https://arxiv.org/abs/1911.01365[Interatomic potential][Simple dense neural network representation]
- https://arxiv.org/abs/1912.02777[Automated tuning][Double quantum dot][Specific charge state][Neural network]
- https://arxiv.org/abs/2312.14322[Data needs][Challenge][Quantum dot device][Automation][Workshop report]
- https://arxiv.org/abs/2002.00054[Implementing a neural network interatomic model][Performance portability][Emerging exascale architecture]
- https://arxiv.org/abs/2002.03452[Estimation][Critical temperature][High-temperature superconductor][AC susceptibility measurement][A pair of Neural Networks]
- https://arxiv.org/abs/2002.04613[Neural network wave function][Sign problem]
- https://arxiv.org/abs/2305.06989[Neural wave function[Superfluid]
- https://arxiv.org/abs/2002.08666[Determination][Semion code threshold][Neural decoder]
- https://arxiv.org/abs/2003.02423[Gate-tunable van der Waals heterostructure][Reconfigurable neural network vision sensor]
- https://arxiv.org/abs/2003.14328[CRYSPNet][Crystal structure prediction][Neural network]
- https://arxiv.org/abs/2004.10994[Physics based approach][Neural network][Enabled design][All-dielectric metasurface]
- https://arxiv.org/abs/2005.06847[Structure prediction][Two-dimensional material][Neural network-driven evolutionary technique]
- https://arxiv.org/abs/2005.12131[MAISE][Construction][Neural network interatomic model][Evolutionary structure optimization]
- https://arxiv.org/abs/2407.08469[Comprehensive convolutional neural network architecture design][Magnetic skyrmion][Domain wall]
- https://arxiv.org/abs/1907.03055[PANNA][Artificial neural network architecture]
- https://arxiv.org/abs/2006.05708[Image reconstruction][Multimode fiber][Simple neural network architecture]
- https://arxiv.org/abs/2407.19483[Nearest-neighbours neural network architecture][Efficient sampling][Statistical physics model]
- https://arxiv.org/abs/2104.02529[Active learning][Element embedding approach][Neural network][Infinite-layer][Perovskite oxide]
- https://arxiv.org/abs/2008.00966[Neural network][Predict icephobic performance]
- https://arxiv.org/abs/1903.04366[Atomic energy mapping][Neural network potential]
- https://arxiv.org/abs/1909.10253[Anharmonic thermodynamics][Vacancy][Neural network potential]
- https://arxiv.org/abs/1909.10134[Hybrid neural network potential][Multilayer graphene]
- https://arxiv.org/abs/2002.04172[Efficient training][Accurate neural network potential][Including atomic force][Taylor expansion][Application to water and a transition-metal oxide]
- https://arxiv.org/abs/2005.09591[Transferability of neural network potential][Varying stoichiometry][Phonon][Thermal conductivity][MnxGey compounds]
- https://arxiv.org/abs/2007.06459[Global optimization][Copper Cluster][ZnO(10-10) Surface][DFT-based neural network potential][Genetic algorithm]
- https://arxiv.org/abs/2008.02572[Enabling ab initio configurational sampling][Multicomponent solid][Long-range interaction][Neural network potential][Active learning]
- https://arxiv.org/abs/2008.05094[Phase stability][Au-Li binary system][Neural network potential]
- https://arxiv.org/abs/2007.00335[Predicting][Oxidation][Spin state][High-dimensional neural network][Lithium manganese oxide spinel]
- https://arxiv.org/abs/2007.00327[Closing the Gap][Theory and experiment][Lithium manganese oxide spinel][High-dimensional neural network potential]
- https://arxiv.org/abs/2009.06484[Fourth-generation high-dimensional neural network potential][Accurate electrostatics][Non-local charge transfer]
- https://arxiv.org/abs/2409.11037[High-dimensional neural network potential][Co3O4]
- https://arxiv.org/abs/2405.00290[Message-passing interatomic potential][Learn non-local electrostatic interaction]
- https://arxiv.org/abs/2011.04604[Systematic approach][Generating accurate neural network potential][Carbon]
- https://arxiv.org/abs/2102.12404[Accurate large-scale simulation][Siliceous zeolite][Neural network potential]
- https://arxiv.org/abs/2106.14583[PFP][Universal neural network potential][Material discovery]
- https://arxiv.org/abs/2107.00963[Real-system nanoparticle][Universal neural network potential][PFP]
- https://arxiv.org/abs/2302.14231[CHGNet][Pretrained universal neural network potential][Charge-informed atomistic modeling]
- https://arxiv.org/abs/2103.10846[Universal neural network][Learning phases and criticalities]
- https://arxiv.org/abs/2312.01290[Evolutionary search][Superconducting phase][Lanthanum-nitrogen-hydrogen system][Universal neural network potential]
- https://arxiv.org/abs/2408.04497[SchrödingerNet][Universal neural network solver][Schrödinger equation]
- https://arxiv.org/abs/2108.05748[Quality of uncertainty estimates][Neural network potential ensemble]
- https://arxiv.org/abs/2203.06283[Prediction][Stable Li-Sn compound][Boosting ab initio searche][Neural network potential]
- https://arxiv.org/abs/2203.16789[Neural network potential][Study point defect properties][Multiple charge state][GaN][Nitrogen vacancy]
- https://arxiv.org/abs/2212.14667[Origin][Performance degradation][High-delithiation][LixCoO2][Insight][Direct atomic simulation][Global neural network potential]
- https://arxiv.org/abs/2301.11612[Neural network potential][Self-trained atomic fingerprint][Test with the mW water potential]
- https://arxiv.org/abs/2304.10812[First-principles modeling][Equilibration dynamics][Hyperthermal product][Surface reaction][Scalable neural network potential]
- https://arxiv.org/abs/2307.11296[Structural analysis][Zirconium oxynitride/water interface][Neural network potential]
- https://arxiv.org/abs/2309.06710[Crystal structure prediction][Neural network potential][Age-fitness pareto genetic algorithm]
- https://arxiv.org/abs/2308.05163[Neural network potential][Modeling nonstoichiometric material][Chromium sulfides Cr(1−x)S]
- https://arxiv.org/abs/2311.05407[Data distillation][Neural network potential][Foundational dataset]
- https://arxiv.org/abs/2402.14640[Structure and thermodynamics][Defect][Na-feldspar][Neural network potential]
- https://arxiv.org/abs/2402.17660[TorchMD-Net 2.0][Fast neural network potential][Molecular simulation]
- https://arxiv.org/abs/2403.09529[General-purpose neural network potential][Ti-Al-Nb alloy][Large-scale molecular dynamics][Ab initio accuracy]
- https://arxiv.org/abs/2404.08413[Lowering][Exponential wall][Accelerating high-entropy alloy catalysts screening][Local surface energy descriptor][Neural network potential]
- https://arxiv.org/abs/2404.12036[Exploring][Premelting transition][Molecular simulation][Neural network potential]
- https://arxiv.org/abs/2407.04526[Peering inside the black box][Learning the relevance][Many-body function][Neural network potential]
- https://arxiv.org/abs/2407.06615[SG-NNP][Species-separated Gaussian neural network potential][Linear elemental scaling][Optimized dimensions][Multi-component materials]
- https://arxiv.org/abs/2407.17452[Thermodynamics][Alkali feldspar solid solution][Varying Al-Si order][Atomistic simulation][Neural network potential]
- https://arxiv.org/abs/2408.11538[Structure and dynamics][Magnetite(001)/water interface][Molecular dynamics simulation][Neural network potential]
- https://arxiv.org/abs/2409.03253[SpinMultiNet][Neural network potential][Incorporating spin degrees of freedom][Multi-task learning]
- https://arxiv.org/abs/2405.03797[Tensor network computation][Capture strict variationality][Volume law behavior][Efficient representation][Neural network states]
- https://arxiv.org/abs/2008.05488[Solving quantum master equation][Deep quantum neural network]
- https://arxiv.org/abs/1907.11333[Entanglement area law][Shallow and deep quantum neural network States]
- https://arxiv.org/abs/2407.05978[Zero-temperature Monte Carlo simulation][Two-dimensional quantum spin glasses][Neural network state]
- https://arxiv.org/abs/2301.07542[Quantum neural network][Inspired hardware adaptable ansatz][Efficient quantum simulation][Chemical system]
- https://arxiv.org/abs/2204.04502[Predicting aggregation][Sequence-defined macromolecule][Recurrent neural network]
- https://arxiv.org/abs/1909.03549[Prediction of optical spectra][Coarse-grained polymer][Sequence generation problem][Recurrent neural networks solution]
- https://arxiv.org/abs/2002.02973[Recurrent neural network][Wavefunction]
- https://arxiv.org/abs/2003.06228[Iterative retraining][Quantum spin model][Recurrent neural network]
- https://arxiv.org/abs/2002.05817[Random telegraph signal analysis][Recurrent neural network]
- https://arxiv.org/abs/2008.07658[Self-supervised learning][Prediction][Microstructure evolution][Recurrent neural network]
- https://arxiv.org/abs/2206.08110[Morphological evolution][Surface diffusion][Convolutional][Recurrent neural network][Extrapolation][Prediction uncertainty]
- https://arxiv.org/abs/2206.12363[Tensor network quantum state][Tensorial recurrent neural network]
- https://arxiv.org/abs/2205.00449[Molecular identification][AFM image][IUPAC nomenclature][Attribute multimodal recurrent neural network]
- https://arxiv.org/abs/2207.14314[Supplementing recurrent neural network wave function][Symmetry][Annealing][Improve accuracy]
- https://arxiv.org/abs/2210.00842[Micromechanics-based recurrent neural networks model][Path-dependent cyclic deformation][Short fiber composite]
- https://arxiv.org/abs/2303.11207[Investigating topological order][Recurrent neural network]
- https://arxiv.org/abs/2310.09434[Learning nonlinear integral operator][Recurrent neural network][Application][Solving integro-differential equation]
- https://arxiv.org/abs/2311.13434[Recurrent neural network][Transfer learning][Elasto-plasticity][Woven composites]
- https://arxiv.org/abs/2404.17583[Physically recurrent neural network][Rate and path-dependent heterogeneous materials][Finite strain framework]
- https://arxiv.org/abs/2409.14042[Recurrent neural network][Prediction][Electronic excitation dynamics]
- https://arxiv.org/abs/2309.04482[Addressing][Accuracy-cost tradeoff][Material property prediction][Teacher-student strategy]
- https://arxiv.org/abs/2407.04557[Structural constraint integration][Generative model][Discovery of quantum material candidates]
- https://arxiv.org/abs/2407.00671[Establishing deep InfoMax][Effective self-supervised learning methodology][Materials informatics]
- https://arxiv.org/abs/2312.14485[Self-supervised generative model][Crystal structure]
- https://arxiv.org/abs/2205.01893[Crystal Twins][Self-supervised learning][Crystalline material property prediction]
- https://arxiv.org/abs/2206.04109[Self-supervised graph neural network][Accurate prediction][Neel temperature]
- https://arxiv.org/abs/2203.10204[Inferring topological transition][Pattern-forming process][Self-supervised learning]
- https://arxiv.org/abs/2402.18286[Self-supervised learning][Electron microscopy][Foundation model][Advanced image analysis]
- https://arxiv.org/abs/2203.13875[Self-supervised machine learning model][Analysis][Nanowire morphologies][Transmission electron microscopy image]
- https://arxiv.org/abs/2311.16652[Augmenting x-ray single particle imaging reconstruction][Self-supervised machine learning]
- https://arxiv.org/abs/2401.05223[Physics guided dual self-supervised learning][Structure-based materials property prediction]
- https://arxiv.org/abs/2405.10135[Self-supervised feature distillation][Design of experiments][Efficient training][Micromechanical deep learning surrogate]
- https://arxiv.org/abs/2408.17255[Self-supervised learning][Crystal property prediction][Denoising]
- https://arxiv.org/abs/2409.08891[Self-supervised learning][Denoising quasiparticle interference data]
- https://arxiv.org/abs/2008.12028[Single-atom level determination][3-dimensional surface atomic structure][Neural network-assisted atomic electron tomography]
- https://arxiv.org/abs/2008.12331[Neural network solver][Small quantum cluster]
- https://arxiv.org/abs/2011.04584[Interpretable][Calibrated neural network][Analysis][Understanding][Inelastic neutron scattering data]
- https://arxiv.org/abs/2011.05199[Random sampling neural network][Quantum many-body Problem]
- https://arxiv.org/abs/2011.11214[Learning the ground state][Non-stoquastic quantum Hamiltonian][Rugged neural network landscape]
- https://arxiv.org/abs/2011.13774[Neural network representation][Electronic structure][Ab initio molecular dynamics]
- https://arxiv.org/abs/2012.00404[Directed graph attention neural network][Utilizing 3D coordinate][Molecular property prediction]
- https://arxiv.org/abs/2101.03016[Hopfield neural network][Magnetic film][Natural learning]
- https://arxiv.org/abs/2307.12111[Noise tailoring][Noise annealing][External noise injection strategies][Memristive Hopfield neural network]
- https://arxiv.org/abs/2101.03181[Miniaturizing neural network][Charge state autotuning][Quantum dot]
- https://arxiv.org/abs/2101.07243[Gauge invariant autoregressive neural network][Quantum lattice model]
- https://arxiv.org/abs/2402.16579[Sparse autoregressive neural network][Classical spin system]
- https://arxiv.org/abs/2304.01996[Autoregressive neural TensorNet][Bridging neural network][Tensor network][Quantum many-body simulation]
- https://arxiv.org/abs/2101.11099[Neural network][Quantum many-body physics][Hands-on tutorial]
- https://arxiv.org/abs/2103.17244[XY Neural Network]
- https://arxiv.org/abs/2104.02741[Conditional physics][Neural network]
- https://arxiv.org/abs/1910.10675[Classical quantum optimization][Neural network quantum state]
- https://arxiv.org/abs/2111.04243[Learning][Compass spin model][Neural network quantum state]
- https://arxiv.org/abs/2206.14307[Systematic improvement][Neural network quantum state][Lanczos recursion]
- https://arxiv.org/abs/2210.16493[Neural network quantum state][Proximal optimization][Ground-state searching scheme][Variational Monte Carlo]
- https://arxiv.org/abs/2301.09923[Lee-Yang theory][Quantum phase transition][Neural network quantum state]
- https://arxiv.org/abs/2304.09504[Ground state properties][Quantum skyrmion][Neural network quantum state]
- https://arxiv.org/abs/2310.05715[Simple linear algebra identity][Optimize large-scale neural network quantum state]
- https://arxiv.org/abs/2403.08184[Quantum skyrmion dynamics][Neural network quantum state]
- https://arxiv.org/abs/2405.04472[Neural network quantum state][Interacting Hofstadter model][Higher local occupation][Long-range interaction]
- https://arxiv.org/abs/2407.20065[Learning eigenstates][Quantum many-body Hamiltonian][Symmetric subspace][Neural network quantum state]
- https://arxiv.org/abs/1802.02944[Neural-network][Kohn-Sham exchange-correlation potential][Out-of-training transferability]
- https://arxiv.org/abs/1909.12852[Fermionic neural-network state][Ab-initio electronic structure]
- https://arxiv.org/abs/2403.11287[Neural-network density functional theory][Variational energy minimization][Phys. Rev. Lett. 133, 076401 (2024)]
- https://arxiv.org/abs/1807.03325[Symmetry][Mny-body excited state][Neural-network quantum state]
- https://arxiv.org/abs/1902.05131[Neural-Network approach][Dissipative quantum many-body dynamics]
- https://arxiv.org/abs/1910.07596[Precise measurement][Quantum observable][Neural-network estimator]
- https://arxiv.org/abs/2002.09246[Efficient neural-network][Variational Monte Carlo scheme][Direct optimization][Excited energy state][Frustrated quantum system]
- https://arxiv.org/abs/2101.05867[Neural-networks model][Force prediction][Multi-principal-element alloy]
- https://arxiv.org/abs/1912.08831[Real time evolution][Neural-network quantum state]
- https://arxiv.org/abs/2008.00118[Two-dimensional spinless lattice fermion][First-quantized deep neural-network][Quantum state]
- https://arxiv.org/abs/2010.14514[U(1) symmetric recurrent neural network][Quantum state reconstruction]
- https://arxiv.org/abs/2010.01358[Neural-network quantum state][Electronic structure][Real solid]
- https://arxiv.org/abs/2103.05017[Correlation-enhanced neural network][Interpretable variational quantum state]
- https://arxiv.org/abs/2103.09146[Compact neural-network][Quantum state representation][Jastrow and stabilizer state]
- https://arxiv.org/abs/2104.10696[Scaling][Neural-network quantum state][Time evolution]
- https://arxiv.org/abs/2105.01054[Stochastic noise][Generalization error][Time propagation][Neural-network quantum state]
- https://arxiv.org/abs/2105.03129[Neural network][Enhanced hybrid quantum many-body dynamical distribution]
- https://arxiv.org/abs/2105.08579[Neural-network quantum state][Spin-1 system][Spin-basis][Parameterization effect][Compactness of representations]
- https://arxiv.org/abs/2112.11957[Neural-network quantum state][Periodic system][Continuous space]
- https://arxiv.org/abs/2202.01704[Investigating network parameter][Neural-network quantum state]
- https://arxiv.org/abs/2305.13132[Neural-network-designed three-qubit gate][Robust][Against charge noise][Crosstalk][Silicon]
- https://arxiv.org/abs/2308.09664[Variational optimization][Amplitude][Neural-network quantum many-body ground state]
- https://arxiv.org/abs/2308.11426[Neural-network force field][Backed nested sampling][Silicon p-T phase diagram]
- https://arxiv.org/abs/2204.10904[Neural-network decoder][Measurement induced phase transition]
- https://arxiv.org/abs/2111.10420[Fermionic wave function][Neural-network][Constrained hidden state]
- https://arxiv.org/abs/2108.10850[Heat transport][Liquid water][First-principles][Deep-neural-network simulation]
- https://arxiv.org/abs/2209.05195[Neural-network quantum state][Two-leg Bose-Hubbard ladder][Magnetic flux]
- https://arxiv.org/abs/2406.00151[Neural-network-supported basis optimizer][Configuration interaction problem][Quantum many-body cluster][Feasibility study][Numerical proof]
- https://arxiv.org/abs/2406.10542[Neural-network-backed effective harmonic potential][Ambient pressure phase][Hafnia][Phys. Rev. B 107, 184111 (2024)]
- https://arxiv.org/abs/2408.02429[Neural-network-enabled molecular dynamics study][HfO2 phase transition]
- https://arxiv.org/abs/2408.06609[Neural-network-based mapping][Optimization framework][High-precision coarse-grained simulation]
- https://arxiv.org/abs/2402.12199[Molecular dynamics simulation][Anisotropic particle][Accelerated][Neural-net predicted interaction]
- https://arxiv.org/abs/2106.14623[Polyconvex][Anisotropic hyperelasticity][Neural network]
- https://arxiv.org/abs/2404.15562[Polyconvex neural network model][Thermoelasticity]
- https://arxiv.org/abs/2306.16270[S2SNet][Pretrained neural network][Superconductivity discovery]
- https://arxiv.org/abs/2106.16043[Robustness][Neural network][Spintronic neuron]
- https://arxiv.org/abs/2108.04244[Distinguishing][Anderson insulator][Many-body localized phase][Space-time snapshot][Neural network]
- https://arxiv.org/abs/2108.05737[Neural network][Universal probe][Many-body localization][Quantum graph]
- https://arxiv.org/abs/2108.08631[Determinant-free fermionic wave function][Feed-forward neural network]
- https://arxiv.org/abs/2202.05183[Discovering quantum phase transition][Fermionic neural network][Phys. Rev. Lett. 130, 036401 (2023)]
- https://arxiv.org/abs/2109.05598[Neural network][Order parameter][Phase transition][High-entropy alloy]
- https://arxiv.org/abs/2109.08861[Improving][Deconvolution of spectrum][Finite temperature][Neural network]
- https://arxiv.org/abs/2109.09655[Impact][Surface][Pore characteristics][Fatigue life][Laser powder bed fusion][Ti-6Al-4V alloy][Neural Network Model]
- https://arxiv.org/abs/2110.03266[Lagrangian neural network][Differential symmetries][Relational inductive bias]
- https://arxiv.org/abs/2405.14645[Lagrangian neural network][Reversible dissipative evolution]
- https://arxiv.org/abs/2110.10748[Melting temperature database][Neural network model][Melting temperature prediction]
- https://arxiv.org/abs/2111.06411[Neural network evolution strategy][Solving quantum sign structure]
- https://arxiv.org/abs/2111.09997[Exploring glassy dynamics][Markov state model][Graph dynamical neural network]
- https://arxiv.org/abs/2405.13098[How glassy are neural networks?]
- https://arxiv.org/abs/2112.04881[Application][Neural network][Exchange-correlation functional interpolation]
- https://arxiv.org/abs/2404.14258[Quantum-enhanced neural exchange-correlation functional]
- https://arxiv.org/abs/2112.09159[Implementation][Binary neural network][Passive array][Magnetic tunnel junction]
- https://arxiv.org/abs/2201.00225[Neural network][Ultrafast time-delayed effect][Exciton-polariton]
- https://arxiv.org/abs/2009.13106[Fast predictive molecular dynamics simulation][Multi-fidelity physics][Informed neural network]
- https://arxiv.org/abs/2011.02512[Robust quantum gate][Smooth pulse][Physics-informed neural network]
- https://arxiv.org/abs/2101.06540[Development][Physically-informed neural network][Interatomic potential][Tantalum]
- https://arxiv.org/abs/2103.14104[Physics-informed neural network][Quantifying][Microstructure][Polycrystalline nickel][Ultrasound data]
- https://arxiv.org/abs/2106.03362[Magnetostatics][Micromagnetics][Physics informed neural network]
- https://arxiv.org/abs/2107.08781[Parameter identification][Damage model][Physics informed neural network]
- https://arxiv.org/abs/2401.15485[Constrained Hamiltonian system][Physics informed neural network][Hamilton-Dirac neural net]
- https://arxiv.org/abs/2301.02191[Physics informed neural network][Charged particle][Surrounded][Conductive boundaries]
- https://arxiv.org/abs/2405.04230[Unveiling][Optimization process][Physics informed neural network][How accurate and competitive can PINNs be?]
- https://arxiv.org/abs/2406.05290[Extremization][Fine tune physics informed neural network][Solving boundary value problem]
- https://arxiv.org/abs/2406.06350[Error analysis][Numerical algorithm][PDE approximation][Hidden-layer concatenated physics informed neural network]
- https://arxiv.org/abs/2408.07027[Multi-soliton solution][Data-driven discovery][Higher-order Burgers' hierarchy equation][Physics informed neural network]
- https://arxiv.org/abs/2401.03948[Reconstruction][Excitation wave][Mechanical deformation][Physics-informed neural network]
- https://arxiv.org/abs/2201.08363[Physics-informed neural network][Modeling rate- and temperature-dependent plasticity]
- https://arxiv.org/abs/2211.04607[First principles physics-informed neural network][Quantum wavefunction][Eigenvalue surface]
- https://arxiv.org/abs/2211.15423[Effective data sampling strategies][Boundary condition constraint][Physics-informed neural network][Identifying material properties][Solid mechanics]
- https://arxiv.org/abs/2308.15640[Identifying constitutive parameter][Complex hyperelastic solid][Physics-informed neural network]
- https://arxiv.org/abs/2008.13654[Extensible structure-informed prediction][Formation energy][Improved accuracy and usability][Neural network]
- https://arxiv.org/abs/2404.02849[Efficient structure-Informed featurization][Property prediction][Ordered][Dilute][Random atomic structure]
- https://arxiv.org/abs/2310.19474[Structure-informed neural network][Boundary observation problem]
- https://arxiv.org/abs/2311.03746[Enhanced physics-informed neural network][Domain scaling][Residual correction method][Multi-frequency elliptic problem]
- https://arxiv.org/abs/2310.09528[Hypernetwork-based meta-learning][Low-rank physics-informed neural network]
- https://arxiv.org/abs/2312.16038[Physics-informed neural network][Solving functional renormalization group][Lattice]
- https://arxiv.org/abs/2402.05067[Multiscale modelling][Physics-informed neural network][Large-scale dynamics][Small-scale prediction][Complex system]
- https://arxiv.org/abs/2402.07231[Ab initio simulation][Thermodynamic properties][Phase transition][Fermi system][Fictitious identical particle][Physics-informed neural network]
- https://arxiv.org/abs/2403.03223[Exact enforcement][Temporal continuity][Sequential physics-informed neural network]
- https://arxiv.org/abs/2403.04094[Multiple scattering simulation][Physics-informed neural network]
- https://arxiv.org/abs/2404.18780[Optimal time sampling][Physics-informed neural network]
- https://arxiv.org/abs/2405.15603[Kronecker-factored approximate curvature][Physics-informed neural network]
- https://arxiv.org/abs/2406.02645[Astral][Training physics-informed neural network][Error majorant]
- https://arxiv.org/abs/2406.04380[Physics-informed neural network][Numerical modeling][Steady-state][Transient electromagnetic problem][Discontinuous media]
- https://arxiv.org/abs/2407.02230[PINNs-MPF][Physics-informed neural network framework][Multi-phase-field simulation][Interface dynamics]
- https://arxiv.org/abs/2407.10654[Inverse physics-informed neural network][Transport model][Porous material]
- https://arxiv.org/abs/2407.20833[Approximating electromagnetic field][Discontinuous media][Single physics-informed neural network]
- https://arxiv.org/abs/2408.04690[Modelling parametric uncertainty][PDEs model][Physics-informed neural network]
- https://arxiv.org/abs/2408.09446[Parameterized physics-informed neural network][Parameterized PDE]
- https://arxiv.org/abs/2408.10965[Monte Carlo physics-informed neural network][Multiscale heat conduction][Phonon Boltzmann transport equation]
- https://arxiv.org/abs/2409.01124[Two-stage initial-value iterative physics-informed neural network][Simulating solitary wave][Nonlinear wave equation][Journal of Computational Physics 505 (2024) 112917]
- https://arxiv.org/abs/2409.03239[DiffGrad][Physics-informed neural network]
- https://arxiv.org/abs/2409.02959[Physics-informed neural network][Incorporating energy dissipation][Phase-field model][Ferroelectric microstructure evolution]
- https://arxiv.org/abs/2409.14248[Higher-order-ReLU-KANs (HRKANs)][Solving physics-informed neural networks (PINNs)][More accurately][Robustly][Faster]
- https://arxiv.org/abs/2410.01340[Response estimation][System identification][Dynamical system][Physics-informed neural network]
- https://arxiv.org/abs/2410.04096[Sinc Kolmogorov-Arnold network][Application][Physics-informed neural network]
- https://arxiv.org/abs/2410.08452[Kolmogorov-Arnold neural network][High-entropy alloys design]
- https://arxiv.org/abs/2410.05744[PINN-MG][Multigrid-inspired hybrid framework combining iterative method][Physics-informed neural network]
- https://arxiv.org/abs/2202.00572[Two-qubit cz gate][Robust][Charge noise][Silicon][Compensating][Crosstalk][Neural network]
- https://arxiv.org/abs/2202.07669[RG-inspired neural network][Computing topological invariant]
- https://arxiv.org/abs/2401.01801[Quatum inspired neural network][Geometric modeling]
- https://arxiv.org/abs/2407.20126[Extreme time extrapolation capabilities][Thermodynamic consistency][Physics-inspired neural network][3D microstructure evolution]
- https://arxiv.org/abs/2202.11851[Ultrasensitive][Ultrafast][Gate-tunable two-dimensional photodetector][Ternary rhombohedral ZnIn2S4][Optical neural network]
- https://arxiv.org/abs/2203.00390[Mitigating][Hubbard sign problem][Complex-valued neural network]
- https://arxiv.org/abs/1909.00669[Ultra-low energy][High speed LIF neuron][Silicon bipolar impact ionization][MOSFET][Spiking neural network]
- https://arxiv.org/abs/2007.15101[Superconducting nanowire][Spiking element][Neural network]
- https://arxiv.org/abs/2009.14462[Nanoscale room-temperature multilayer skyrmionic synapse][Deep spiking neural network]
- https://arxiv.org/abs/2203.02171[Bilayer-skyrmion][Design][Neuron][Synapse][Spiking neural network]
- https://arxiv.org/abs/2211.06630[Design][Spintronics-based neuronal][Synaptic device][Spiking neural network circuit]
- https://arxiv.org/abs/2307.13320[Autonomous neural information processing][Dynamical memristor circuit]
- https://arxiv.org/abs/2302.08458[Solid state neuroscience][Spiking neural network][Time matter]
- https://arxiv.org/abs/2304.04794[Stochastic domain wall-magnetic tunnel junction][Artificial Neuron]Noise-resilient spiking neural network]
- https://arxiv.org/abs/2304.10899[Electromechanical memcapacitive neuron][Energy-efficient spiking neural network]
- https://arxiv.org/abs/2310.07824[On-chip trainable neuron circuit][SFQ-based spiking neural network]
- https://arxiv.org/abs/2311.07787[Hybrid synaptic structure][Spiking neural network realization]
- https://arxiv.org/abs/2402.03767[Magnetic field gated][Current controlled][Spintronic mem-transistor neuron][Spiking neural network]
- https://arxiv.org/abs/2402.19139[Unified evaluation framework][Spiking neural network hardware accelerator][Emerging non-volatile memory device]
- https://arxiv.org/abs/2405.00700[Oxygen vacancies][Modulated VO2][Neurons and spiking neural network construction]
- https://arxiv.org/abs/2203.07537[Denoising][Feature extraction][Photoemission spectra][Variational auto-encoder neural network]
- https://arxiv.org/abs/2203.08607[Developing potential energy surface][Graphene-based 2D-3D interface][Modified high dimensional neural network][Application][Energy storage]
- https://arxiv.org/abs/2203.09046[Memristive deep belief neural network][Silicon synapses]
- https://arxiv.org/abs/2204.04529[Learning hyperelastic anisotropy][Data via a tensor basis neural network]
- https://arxiv.org/abs/2204.11904[Neural network representation][Minimally entangled typical thermal state]
- https://arxiv.org/abs/2406.19957[Neural network representation][Multiphase equations of state]
- https://arxiv.org/abs/2204.13724[Predicting magnetic edge behaviour][Graphene][Neural network]
- https://arxiv.org/abs/2206.01693[Three-dimensional microstructure generation][Generative adversarial neural network][Continuum micromechanics]
- https://arxiv.org/abs/2312.02479[Application][Domain adversarial neural network][Phase transition][3D Potts model]
- https://arxiv.org/abs/2206.02496[Noise signal][Input data][Self-organized neural network]
- https://arxiv.org/abs/2206.03701[Prediction][Electromotive force][Magnetic shape memory alloy][MSMA][Constitutive model][Generalized regression neural network]
- https://arxiv.org/abs/2206.07697[MACE][Higher order equivariant message passing neural network][Fast][Accurate][Force field]
- https://arxiv.org/abs/2206.07753[Sample generation][Spin-fermion model][Neural network]
- https://arxiv.org/abs/2206.10908[Neural network][Quick access][Digital twin][Scanning physical properties measurement]
- https://arxiv.org/abs/2207.06512[Generalized framework][Microstructural optimization][Neural network]
- https://arxiv.org/abs/2207.07428[Automatic detection][Equiaxed dendrites][Computer vision neural network]
- https://arxiv.org/abs/2208.02235[Quantum-inspired][Tensor neural network][Partial differential equation]
- https://arxiv.org/abs/2403.06084[pETNNs][Partial evolutionary tensor neural network][Solving time-dependent partial differential equation]
- https://arxiv.org/abs/2403.15073[Inclusion of charge and spin states][Cartesian tensor neural network potential]
- https://arxiv.org/abs/2307.02327[Equivariant graph neural network interatomic potential][Green-Kubo thermal conductivity][Phase change material]
- https://arxiv.org/abs/2402.03789[Scalable parallel algorithm][Graph neural network interatomic potential][Molecular dynamics simulation]
- https://arxiv.org/abs/2305.11805[PANNA 2.0][Efficient neural network interatomic potential][New architecture]
- https://arxiv.org/abs/2402.16628[Single neuromorphic memristor][Closely emulates multiple synaptic mechanism][Energy efficient neural network]
- https://arxiv.org/abs/2408.14680[On-chip learning][Memristor-based neural network][Assessing accuracy][Efficiency][Device variation][Conductance error][Input noise]
- https://arxiv.org/abs/2112.04636[Artificial neural network interatomic potential][Dislocation][Fracture][Molybdenum]
- https://arxiv.org/abs/1912.01398[TeaNet][Universal neural network interatomic potential][Inspired][[Iterative electronic relaxation]
- https://arxiv.org/abs/2409.01315[Multi-frequency neural born iterative method][Solving 2-D inverse scattering problem]
- https://arxiv.org/abs/2102.04085[Automated approach][Developing neural network interatomic potential][FLAME]
- https://arxiv.org/abs/2208.11863[Ab initio construction][Full phase diagram][MgO-CaO eutectic system][Neural network interatomic potential]
- https://arxiv.org/abs/2310.02904[Spline-based neural network interatomic potential][Blending classical][Machine learning model]
- https://arxiv.org/abs/2312.10856[Neural network interatomic potential][Open surface nano-mechanics application]
- https://arxiv.org/abs/2402.13984[Stability-aware training][Neural network interatomic potential][Differentiable Boltzmann estimator]
- https://arxiv.org/abs/2404.11587[Entropy-driven polymorphic stability][Aspirin][Accurate neural network interatomic potential]
- https://arxiv.org/abs/2208.12984[Si plate radius influence][Photoacoustic signal][Processed][Neural network]
- https://arxiv.org/abs/2209.01908[Ensemble][Pre-trained neural network][Segmentation][Quality detection][Transmission electron microscopy image]
- https://arxiv.org/abs/2308.08934[Data imbalance][Molecular property prediction][Pre-training]
- https://arxiv.org/abs/2209.05609[Neural network approach][Predict Gibbs free energy][Ternary solid solution]
- https://arxiv.org/abs/2210.00336[Uncertainty-aware prediction][Molecular X-ray absorption spectra][Neural network ensemble]
- https://arxiv.org/abs/2210.10507[Predicting oxide glass properties][Low complexity neural network][Physical and Chemical descriptors]
- https://arxiv.org/abs/2211.02183[Photoacoustic characterization][TiO2 thin-film][Deposited][Silicon substrate][Neural network]
- https://arxiv.org/abs/2210.13955[General framework][E(3)-equivariant neural network representation][Density functional theory Hamiltonian]
- https://arxiv.org/abs/2305.03804[Equivariant neural network][Spin dynamics simulation][Itinerant magnet]
- https://arxiv.org/abs/2211.03198[Gauge equivariant neural network][2+1D U(1) gauge theory simulation][Hamiltonian formulation]
- https://arxiv.org/abs/2306.01558[Accelerating][Electronic-structure calculation][Magnetic system][Equivariant neural network]
- https://arxiv.org/abs/2312.05388[Higher-order equivariant neural network][Charge density prediction]
- https://arxiv.org/abs/2312.14680[DeepGreen][Equivariant neural network][Green's function][Molecules and materials]
- https://arxiv.org/abs/2402.04864[Equivariant neural network force field][Magnetic material]
- https://arxiv.org/abs/2402.16204[Transferable water potential][Equivariant neural network]
- https://arxiv.org/abs/2403.05249[Representing electronic wave function][Sign equivariant neural network]
- https://arxiv.org/abs/2309.00195[Uncertainty estimate][Equivariant-neural-network-ensembles interatomic potential]
- https://arxiv.org/abs/2211.16684[Capturing long-range interaction][Reciprocal space neural network]
- https://arxiv.org/abs/2305.13992[Liouville space neural network representation][Density matrices]
- https://arxiv.org/abs/2301.02683[Classifying topological neural network][Quantum state][Diffusion map]
- https://arxiv.org/abs/2302.02523[Principle][Learning sign rule][Neural network][Qubit lattice model]
- https://arxiv.org/abs/2302.04906[High pressure and temperature][Neural network][Reactive force field][Energetic material]
- https://arxiv.org/abs/2302.12629[Quantifying noise limitations][Neural network segmentation][High-resolution transmission electron microscopy]
- https://arxiv.org/abs/2303.02876[Finding metastable skyrmionic structure][Metaheuristic perturbation-driven neural network]
- https://arxiv.org/abs/2302.14397[Neural network][Constitutive modeling][Universal function approximator][Advanced model][Integration of Physics]
- https://arxiv.org/abs/2303.11699[Neural network][Trained][Synthetically generated crystal][Extract structural information][ICSD powder X-ray diffractogram]
- https://arxiv.org/abs/2304.02957[Neural network kinetics][Diffusion multiplicity][B2 ordering][Compositionally complex alloy]
- https://arxiv.org/abs/2305.00776[Characterizing exceptional point][Neural network]
- https://arxiv.org/abs/2305.00003[Neural network accelerated process design][Polycrystalline microstructure]
- https://arxiv.org/abs/2305.07660[Using limited neural network][Assess relative mechanistic influence][Shock heating][Granular solid]
- https://arxiv.org/abs/2305.19546[Prediction][Born effective charge][Neural network][Ion migration][Electric field][Application][Crystalline][Amorphous][Li3PO4]
- https://arxiv.org/abs/2306.08383[Neural network][Tool][Design][Amorphous metal alloy][Desired elastoplastic properties][Metals 13(4), 812 (2023)]
- https://arxiv.org/abs/2307.00911[Reactive neural network framework][Water-loaded acidic zeolite]
- https://arxiv.org/abs/2307.02212[Electric polarization][Many-body neural network ansatz][Phys. Rev. Lett. 132, 176401 (2024)]
- https://arxiv.org/abs/2308.12722[Accelerated neural network training][Dimensionality reduction][High-throughput screening][Topological material]
- https://arxiv.org/abs/2310.02122[Minimalist neural networks training][Phase classification][Diluted-Ising model]
- https://arxiv.org/abs/2310.05607[Neural network variational Monte Carlo][Positronic chemistry]
- https://arxiv.org/abs/2310.08578[Neural network approach][Quasiparticle dispersion][Doped antiferromagnet]
- https://arxiv.org/abs/2310.10434[Equivariant matrix function][Neural network]
- https://arxiv.org/abs/2310.10020[Optimized nanodevice fabrication][Clean transfer][Graphene][Polymer mixture][Experiment][Neural network based simulation]
- https://arxiv.org/abs/2405.04524[Neural network based deep learning analysis][Semiconductor quantum dot qubit][Automated control]
- https://arxiv.org/abs/2405.19008[Mechanism and kinetics][Sodium diffusion][Na-feldspar][Neural network based atomistic simulation]
- https://arxiv.org/abs/2310.11245[Neural network approach][Rapid prediction][Metal-supported borophene properties]
- https://arxiv.org/abs/2310.16491[TSONN][Time-stepping-oriented neural network][Solving partial differential equation]
- https://arxiv.org/abs/2311.13799[Neural network thermodynamics]
- https://arxiv.org/abs/2312.05846[Mie sensing][Neural network][Recognition][Nano-object parameter][Invisibility point][Restricted model]
- https://arxiv.org/abs/2312.13511[Symmetry-enforcing neural network][Application][Constitutive modeling]
- https://arxiv.org/abs/2401.06936[Accelerated sampling][Rare event][Neural network bias potential]
- https://arxiv.org/abs/2401.09301[Material informatics][Neural network][Ab-Initio electron charge densities][Role of transfer learning]
- https://arxiv.org/abs/2401.10190[Kaczmarz-inspired approach][Accelerate][Optimization][Neural network wavefunction]
- https://arxiv.org/abs/2401.14676[Spintronic virtual neural network][Voltage controlled ferromagnet][Associative memory]
- https://arxiv.org/abs/2402.02362[Unification][Symmetries inside neural network][Transformer][Feedforward][Neural ODE]
- https://arxiv.org/abs/2406.00091[Transformer neural network][Qquantum simulator][Hybrid approach][Simulating strongly correlated system]
- https://arxiv.org/abs/2407.06039[Predicting VCSEL emission properties][Transformer neural network]
- https://arxiv.org/abs/2403.01776[Hybrid data-driven][Physics-informed regularized learning][Cyclic plasticity][Neural network]
- https://arxiv.org/abs/2403.03286[Neural network backflow][Ab-initio quantum chemistry]
- https://arxiv.org/abs/2403.16819[Neural network approach][Two-body system][Spin and isospin degrees of freedom]
- https://arxiv.org/abs/2404.15118[Identifying phase transition][Physical system][Neural network]Neural architecture search perspective]
- https://arxiv.org/abs/2404.03863[Establishing][Relationship][Generalized crystallographic texture][Macroscopic yield surface][Partial input convex neural network]
- https://arxiv.org/abs/2405.16769[Learning phase transition][Siamese neural network]
- https://arxiv.org/abs/2406.08318[Invariant multiscale neural network][Data-scarce scientific application]
- https://arxiv.org/abs/2406.15873[NeuralSCF][Neural network self-consistent field][Density functional theory]
- https://arxiv.org/abs/2406.17645[Simulating moire quantum matter][Neural network]
- https://arxiv.org/abs/2408.03263[Multiscale modeling framework][Constrained fluid][Complex boundaries][Twin neural networks]
- https://arxiv.org/abs/2408.04073[Accelerating crystal structure search][Active learning][Neural network][Rapid relaxation]
- https://arxiv.org/abs/2408.11395[Short introduction][Neural network][Application][Earth and materials science]
- https://arxiv.org/abs/2409.20320[Experimental online quantum dot][Charge autotuning][Neural network]
- https://arxiv.org/abs/2408.16915[SOLAX][Python solver][Fermionic quantum system][Neural network support]
- https://arxiv.org/abs/2409.05240[Physics-enforced neural network][Predict polymer melt viscosity]
- https://arxiv.org/abs/2410.07451[Collective variables][Neural network][Empirical time evolution][Scaling law]
- https://arxiv.org/abs/2410.08882[Unified quantum framework][Electrons and ions][Self-consistent harmonic approximation][Neural network curved manifold]
- https://arxiv.org/abs/2212.00782[Variational neural-network ansatz][Continuum quantum field theory]
- https://arxiv.org/abs/2401.14243[Variational neural and tensor network approximations][Thermal state]
- https://arxiv.org/abs/2406.00193[Learning topological state][Randomized measurement][Variational tensor network tomography]
- https://arxiv.org/abs/2212.02204[Neural quantum state][Volume-law ground state]
- https://arxiv.org/abs/1909.11150[Exascale deep learning][Scientific inverse problem]
- https://arxiv.org/abs/2409.08362[Deep Ritz][Finite element method][Neural network method][Trained with finite elements]
- https://arxiv.org/abs/2106.11623[Deep neural network][Inverse problem][Mesoscopic physics][Characterization][Disorder configuration][Quantum transport property]
- https://arxiv.org/abs/1810.12183[Solving inverse problem][Multi-scale deep convolutional neural network]
- https://arxiv.org/abs/2304.13860[Enhancing inverse problem solution][Accurate surrogate simulator][Promising candidate]
- https://arxiv.org/abs/2107.01584[Decoding][DC and optical conductivities][Disordered MoS2 film][Inverse problem]
- https://arxiv.org/abs/2402.09338[Neural network][Asymptotic behaviour][Resolution][Inverse problem]
- https://arxiv.org/abs/2404.02387[Inversion problem][Optical spectrum data][Physics-guided machine learning]
- https://arxiv.org/abs/2201.00722[Predicting peak stress][Microstructured Material][Convolutional encoder-decoder learning]
- https://arxiv.org/abs/2002.03032[Automatic design][Mechanical metamaterial actuator]
- https://arxiv.org/abs/2001.11814[Complete solution][Tight binding model][Cayley tree]
- https://arxiv.org/abs/1903.09499[Learning magnetization dynamics]
- https://arxiv.org/abs/1709.07082[Machine learning][Molecular properties][Locality][Active learning]
- https://arxiv.org/abs/2109.10890[Machine-learning enabled search][Next-generation catalyst][Hydrogen evolution reaction]
- https://arxiv.org/abs/2211.10342[Machine-learning enabled optimization][Atomic structure][Fractional existence]
- https://arxiv.org/abs/2405.14776[Kinetics of orbital ordering][Cooperative Jahn-Teller model][Machine-learning enabled large-scale simulation]
- https://arxiv.org/abs/2204.00735[Genetic programming-based learning][Carbon interatomic potential][Materials discovery]
- https://arxiv.org/abs/2110.06888[Exploring causal physical mechanism][Non-gaussian linear model][Deep kernel learning][Ferroelectric domain structure]
- https://arxiv.org/abs/2410.03173[Rapid optimization][High dimensional space][Deep kernel learning][Augmented genetic algorithm]
- https://arxiv.org/abs/2303.14554[Deep kernel method][Learn better][From cards to process optimization]
- https://arxiv.org/abs/1810.07310[Prediction][Atomization energy][Graph kernel][Active learning]
- https://arxiv.org/abs/1806.10567[Accelerating high-throughput searche][New alloys][Active learning][Interatomic potential]
- https://arxiv.org/abs/2403.18298[Deciphering chemical ordering][High entropy material][Machine learning-accelerated high-throughput cluster expansion approach]
- https://arxiv.org/abs/1810.11890[Active learning][Uniformly accurate inter-atomic potential][Materials Simulation]
- https://arxiv.org/abs/1904.10692[Speeding up][Quantum few-body calculation][Active learning]
- https://arxiv.org/abs/1909.11654[Active Learning][Coarse-grained energy landscape][Water cluster][Sparse training data]
- https://arxiv.org/abs/1912.04596[Active-learning-based efficient prediction][Ab-initio atomic energy][Fe random grain boundary model][millions of atoms]
- https://arxiv.org/abs/2004.13158[Operando active learning][Interatomic interaction][Large-scale simulation]
- https://arxiv.org/abs/2005.03014[Active learning][One-dimensional density functional theory]
- https://arxiv.org/abs/2005.11488[Hierarchical active-learning framework][Classifying structural motif][Atomic resolution microscopy]
- https://arxiv.org/abs/2006.03674[Active learning][Neural network model][Gold Cluster][Bulk][Sparse first principles training data]
- https://arxiv.org/abs/2007.08555[MLIP package][Moment tensor potential][MPI][Active learning]
- https://arxiv.org/abs/2010.06896[Efficient estimation][Material property curves and surfaces][Active learning]
- https://arxiv.org/abs/2103.00608[Active learning based generative design][Discovery][Wide bandgap material]
- https://arxiv.org/abs/2108.06037[Experimental discovery][Structure-property relationship][Ferroelectric material][Active learning]
- https://arxiv.org/abs/2110.08136[Active learning][Molecular dynamics simulation][High melting temperature alloy]
- https://arxiv.org/abs/2111.09659[Polymer sequence design][Active learning]
- https://arxiv.org/abs/2203.10181[Active learning][Open experimental environment][Selecting][Right information channel(s)][Predictability][Deep kernel learning]
- https://arxiv.org/abs/2204.05838[Benchmarking active learning strategies][Materials optimization][Discovery]
- https://arxiv.org/abs/2208.05912[Atomistic fracture][bcc iron][Active learning][Gaussian approximation potential]
- https://arxiv.org/abs/2208.05444[Rapid exploration][32.5M compound chemical space][Active learning][Discover density functional approximation][Iinsensitive][Synthetically][Accessible][Transitional metal chromophore]
- https://arxiv.org/abs/2210.16364[Uncertainty driven active learning][Coarse grained free energy model]
- https://arxiv.org/abs/2211.07881[ET-AL][Entropy-targeted active learning][Bias mitigation][Materials data]
- https://arxiv.org/abs/2212.07310[Exploring][Microstructural origin][Conductivity][Hysteresis][Metal halide perovskite][Active learning driven automated scanning probe microscopy]
- https://arxiv.org/abs/2212.08716[Active learning strategies][Atomic cluster expansion model]
- https://arxiv.org/abs/2302.01603[Towards active learning][Stopping criterion][Sequential sampling][Grain boundary degrees of freedom]
- https://arxiv.org/abs/2303.12924[Active learning][Sensitivity analysis][γ′(L12) precipitate morphology][Ternary Co-based superalloy]
- https://arxiv.org/abs/2309.06786[Lattice thermal conductivity][First principles][Active learning][Gaussian process regression]
- https://arxiv.org/abs/2310.16168[Role of multifidelity data][Sequential active learning][Materials discovery campaign][Case study][Electronic bandgap]
- https://arxiv.org/abs/2311.01987[Generalization][Graph-based active learning][Relaxation strategies][Across materials]
- https://arxiv.org/abs/2312.05343[Active learning approach][Simulation][Strongly correlated matter][Ghost Gutzwiller approximation]
- https://arxiv.org/abs/2401.05568[Phase discovery][Active learning][Application][Structural phase transition][Equiatomic NiTi]
- https://arxiv.org/abs/2401.16487[Active learning][Boltzmann sampler][Potential energies][Quantum mechanical accuracy]
- https://arxiv.org/abs/2402.15582[Probabilistic prediction][Material stability][Integrating convex hull][Active learning]
- https://arxiv.org/abs/2403.06329[Active learning][Rapid targeted synthesis][Compositionally complex alloy]
- https://arxiv.org/abs/2404.07074[Multiscale structure-property discovery][Active learning][Scanning tunneling microscopy]
- https://arxiv.org/abs/2407.06051[Foam][3D spatially programmed mechanics][Autonomous active learning][Viscous thread printing]
- https://arxiv.org/abs/2407.18731[Exploring quantum active learning][Materials design and discovery]
- https://arxiv.org/abs/2408.02071[Scientific exploration][Expert knowledge][SEEK][Autonomous scanning probe microscopy][Active learning]
- https://arxiv.org/abs/2409.07042[Active learning][Discovering complex phase diagram][Gaussian processes]
- https://arxiv.org/abs/2007.04145[Structure motif centric learning framework][Inorganic crystalline system]
- https://arxiv.org/abs/2103.01462[Neural evolution structure generation][High entropy alloy]
- https://arxiv.org/abs/2404.15745[Reconstructing][Magnetic Field][Arbitrary Domain][Data-driven Bayesian Method][Numerical Simulation]
- https://arxiv.org/abs/1704.07423[High-dimensional materials and process optimization][Data-driven experimental design][Well-calibrated uncertainty estimates]
- https://arxiv.org/abs/1806.07989[Data-driven][Magnetic two-dimensional material]
- https://arxiv.org/abs/1806.09284[Two pressure-induced superconducting transitions][Explored][Data-driven materials search][New approach][Develop novel functional materials][Thermoelectric][Superconducting]
- https://arxiv.org/abs/1901.04832[Exploring the 3D architecture][Deep material network][Data-driven multiscale mechanics]
- https://arxiv.org/abs/2402.11102[Universal design methodology][Printable microstructural material][New deep generative learning model][Application][Piezocomposite]
- https://arxiv.org/abs/2407.06489[T2MAT (text-to-materials)][Universal framework][Generating material structure][Goal properties][Single sentence]
- https://arxiv.org/abs/1910.11499[Deep generative model][Inorganic chemical composition]
- https://arxiv.org/abs/2403.11686[Crystalformer][Infinitely connected attention][Periodic structure encoding]
- https://arxiv.org/abs/1908.03665[Robust data-driven approach][Predicting][Configurational energy][High entropy alloy]
- https://arxiv.org/abs/1909.00949[Data-driven approach][Encoding][Decoding][3-D crystal structure]
- https://arxiv.org/abs/1902.10282[Prediction of activation energy barrier][Island diffusion process][Data-driven approach]
- https://arxiv.org/abs/2102.01131[Data-driven approach][Parameterize SCAN+U][Accurate description][3d transition metal oxide thermochemistry]
- https://arxiv.org/abs/2108.13206[Data-driven approach][Identification][Novel organic ferroelectrics]
- https://arxiv.org/abs/2408.14804[Data-driven approach][Learning optimal form][Constitutive relation][Models describing lithium plating][Battery cell]
- https://arxiv.org/abs/1902.09395[Data-driven material Model][Atomistic simulation]
- https://arxiv.org/abs/1902.09770[Data-driven exploration][Pressure-induced superconductivity][AgIn5Se8]
- https://arxiv.org/abs/1907.05644[Data-driven materials science][Status][Challenge][Perspective]
- https://arxiv.org/abs/2002.12208[Gaussian process state][Data-driven representation][Quantum many-body physics]
- https://arxiv.org/abs/2005.11596[Data-driven][Kinetic energy density fitting][Orbital-free DFT][Linear vs Gaussian process regression]
- https://arxiv.org/abs/2010.01765[Voting data-driven regression learning][Discovery][Functional material][Application][Two-dimensional ferroelectric material]
- https://arxiv.org/abs/2006.07523[Data-driven determination][Spin Hamiltonian parameter][Uncertainty][Zigzag-chain compound KCu4P3O12]
- https://arxiv.org/abs/2007.01831[JARVIS][Integrated infrastructure][Data-driven Materials Design]
- https://arxiv.org/abs/2305.11842[Recent progress][JARVIS infrastructure][Next-generation data-driven materials design]
- https://arxiv.org/abs/2011.03191[Shape-dependent local strain][Gold nanorod][Data-driven atomic-resolution electron microscopy analysis]
- https://arxiv.org/abs/2011.08551[Variation free density functional theory][Data-driven][Stochastic optimization]
- https://arxiv.org/abs/2102.01154[Optimizing accuracy and efficacy][Data-driven materials discovery][Solar production of hydrogen]
- https://arxiv.org/abs/2102.02263[Robust model benchmarking][Bias-imbalance][Data-driven materials science][MODNet]
- https://arxiv.org/abs/2102.07320[Data-driven analysis][Electronic-structure factors controlling][Work function][Perovskite oxide]
- https://arxiv.org/abs/2103.04875[Data-driven sensitivity analysis][Total-reflection high-energy positron diffraction (TRHEPD) experiment][Si4O5N3/6H-SiC(0001)-(31/2x31/2)R30]
- https://arxiv.org/abs/2103.07957[JAMIP][Artificial-intelligence][Data-driven infrastructure][Computational materials informatics]
- https://arxiv.org/abs/2103.12396[Data-driven][Rate-dependent][Fracture mechanics]
- https://arxiv.org/abs/2105.12784[Data-driven relationship][Atomic structure][Physical property]Holistic view][Materials science fundamental]
- https://arxiv.org/abs/2107.13147[Mechanical cloak][Data-Driven][Aperiodic metamaterial design]
- https://arxiv.org/abs/2108.02236[Data-driven analysis][Grain-growth kinetics][Duplex and triplex systems]
- https://arxiv.org/abs/2108.04883[Data-driven peridynamic continuum model][Upscaling molecular dynamics]
- https://arxiv.org/abs/2108.06667[Realizing][Data-driven][Computational discovery][Metal-organic framework Catalyst]
- https://arxiv.org/abs/2110.02070[Data-driven electron microscopy][Electron diffraction imaging][Materials structural properties]
- https://arxiv.org/abs/1803.05035[Autonomous data-driven design][Inorganic material][AFLOW]
- https://arxiv.org/abs/2201.11289[High throughput data-driven design][Laser crystallized 2D MoS2 chemical sensor]
- https://arxiv.org/abs/2101.10773[Data-driven design][New class][Rare-earth free permanent magnet]
- https://arxiv.org/abs/2109.10798[Data-driven design][Novel halide perovskite alloy]
- https://arxiv.org/abs/2211.08014[Data-driven design][New catalytic material][Methane oxidation][Site isolation concept]
- https://arxiv.org/abs/2307.05506[Data-driven design][Metamaterial][Multiscale system][Review]
- https://arxiv.org/abs/2310.03633[Data-driven design][Multilayer hyperbolic metamaterial][Near-field thermal radiative modulator][High modulation contrast]
- https://arxiv.org/abs/2204.09827[Data-driven design][New organic semiconductor][Electronic structure chart]
- https://arxiv.org/abs/2312.12694[Data-driven design][High pressure hydride superconductor][DFT][Deep learning]
- https://arxiv.org/abs/2405.09897[Towards informatics-driven design][Nuclear waste form]
- https://arxiv.org/abs/2110.01366[Data-driven quest][Two-dimensional][Non-van der Waals material]
- https://arxiv.org/abs/2110.12870[Data-driven][Intrinsic localized mode detection][Classification][One-dimensional crystal lattice model]
- https://arxiv.org/abs/2111.05129[Thermodynamics][Dielectric response][BaTiO3][Data-driven modeling]
- https://arxiv.org/abs/2201.11106[Data-driven][Constrained optimization][Semi-local exchange][Non-local correlation functional][Materials and surface chemistry]
- https://arxiv.org/abs/2201.11647[Data-driven time propagation][Quantum system][Neural network]
- https://arxiv.org/abs/2202.02380[Data-driven materials discovery][Synthesis][Machine learning method]
- https://arxiv.org/abs/2202.11355[Superconductive material][MgB2-like structure][Data-driven screening]
- https://arxiv.org/abs/2205.06674[Model-free data-driven viscoelasticity][Frequency domain]
- https://arxiv.org/abs/2202.01373[Data-driven discovery][High performance][Layered van der Waals piezoelectric NbOI2]
- https://arxiv.org/abs/2311.14196[Data-driven discovery][Dynamics][Time-resolved coherent scattering]
- https://arxiv.org/abs/2206.12159[Data-driven discovery][Novel 2D material][Deep generative model]
- https://arxiv.org/abs/2405.11379[Symmetry-guided data-driven discovery][Native quantum defect][Two-dimensional material]
- https://arxiv.org/abs/2405.12643[Data-driven discovery][Robust optimization][Semiconductor nanowire laser]
- https://arxiv.org/abs/2206.13596[Data-driven][Thiele equation approach][Solving][Full nonlinear spin-torque vortex oscillator dynamics]
- https://arxiv.org/abs/2208.09177[Identification][Dislocation reaction kinetics][Complex dislocation network][Continuum modeling][Data-driven method]
- https://arxiv.org/abs/2209.02046[Data-driven prediction][Room temperature density][Multicomponent silicate-based glasses]
- https://arxiv.org/abs/2302.03843[Data-driven prediction][Complex crystal structure][Dense lithium]
- https://arxiv.org/abs/2408.10814[Data-driven prediction][Structure][Metal-organic framework]
- https://arxiv.org/abs/2209.10709[Data-driven interpretation][Stability][Molecular crystal]
- https://arxiv.org/abs/1904.05859[Big-data-driven materials science][FAIR data infrastructure]
- https://arxiv.org/abs/2210.06027[Experimental data management platform][Data-driven investigation][Combinatorial alloy thin film]
- https://arxiv.org/abs/2211.12971[Cooperative data-driven modeling]
- https://arxiv.org/abs/2303.06743[Data-driven][Statistical reduced-order modeling][Quantification][Polycrystal mechanics][Leading to porosity-based ductile damage]
- https://arxiv.org/abs/2303.14033[Data-driven estimation][Transfer integral][Undoped cuprate]
- https://arxiv.org/abs/2304.04809[Data-driven framework][Structure-property correlation][Ordered and disordered cellular metamaterials]
- https://arxiv.org/abs/2304.13897[Physics-informed data-driven discovery][Constitutive model][Application][Strain-rate-sensitive soft material]
- https://arxiv.org/abs/2305.01806[Data-driven][Physics-informed descriptor][Cation ordering][Multicomponent oxide]
- https://arxiv.org/abs/2305.19279[Data-driven game][Computational mechanics]
- https://arxiv.org/abs/2306.11071[ColabFit exchange][Open-access dataset][Data-driven interatomic potential]
- https://arxiv.org/abs/2310.08593[Data-driven method][Diffusivity prediction][Nuclear fuel]
- https://arxiv.org/abs/2310.10695[Data-driven score-based model][Generating stable structure][Adaptive crystal cell]
- https://arxiv.org/abs/2311.05571[Effective data-driven collective variable][Free energy calculation][Metadynamics of path]
- https://arxiv.org/abs/2401.06070[Peridynamic neural operator][Data-driven nonlocal constitutive model][Complex material response]
- https://arxiv.org/abs/2408.15097[Data-driven nonlinear deformation design][3D-printable shell]
- https://arxiv.org/abs/2401.11393[Data-driven compression][Electron-phonon interaction]
- https://arxiv.org/abs/2402.07289[Virtual reassembling][3D fragment][Data-driven analysis][Fracture mechanism][Composite material]
- https://arxiv.org/abs/2402.13685[Data-driven forecasting][Non-equilibrium solid-state dynamics]
- https://arxiv.org/abs/2402.16433[Data-driven acceleration][Multi-physics simulation]
- https://arxiv.org/abs/2402.19462[Accelerating materials discovery][Polymer solar cell][Data-driven insight][Natural language processing]
- https://arxiv.org/abs/2403.15625[Integrated workflows and interfaces][data-driven semi-empirical electronic structure calculation]
- https://arxiv.org/abs/2402.12520[Data-driven study][Composition-dependent phase compatibility][NiTi shape memory alloy]
- https://arxiv.org/abs/2406.11004[Data-driven study][Enthalpy][Mixing][Liquid phase]
- https://arxiv.org/abs/2406.19662[Finite basis Kolmogorov-Arnold network][Domain decomposition][Data-driven][Physics-informed problem]
- https://arxiv.org/abs/2407.00975[Data-driven approximation][Topological insulator system]
- https://arxiv.org/abs/2407.05050[Sparse identification][Quasipotential][Combined data-driven method]
- https://arxiv.org/abs/2409.01989[Improving electrolyte performance][Target cathode loading][Interpretable data-driven approach]
- https://arxiv.org/abs/2409.09092[Data-driven virtual test-bed][Blown powder][Directed energy deposition process]
- https://arxiv.org/abs/2409.11080[Data-driven stochastic 3D modeling][Nanoporous binder-conductive additive phase][Battery cathode]
- https://arxiv.org/abs/2409.11782[Smart Data-driven GRU predictor][SnO2 thin films Characteristics]
- https://arxiv.org/abs/2209.13700[Data driven approach][Cross-slip modelling][Continuum dislocation dynamics]
- https://arxiv.org/abs/2403.18441[Physics and data driven model][Prediction][Residual stresses][Machining]
- https://arxiv.org/abs/1812.01966[Managing uncertainty][Data-derived densities][Accelerate density functional theory]
- https://arxiv.org/abs/1808.02114[Definition of a scoring parameter][Identify][low-dimensional material][Component]
- https://arxiv.org/abs/1808.06935[Smart energy model][Atomistic simulation][DFT-driven multifidelity approach]
- https://arxiv.org/abs/2205.12074[Electronic descriptor][Supervised spectroscopic prediction]
- https://arxiv.org/abs/1811.12423[Variational optimization][AI era][Computational graph state][Supervised wave-function optimization]
- https://arxiv.org/abs/2207.01079[DiSCoMaT][Distantly supervised composition extraction][Table][Material science article]
- https://arxiv.org/abs/1902.06836[Graph dynamical network][Unsupervised learning][Atomic scale dynamics]
- https://arxiv.org/abs/1903.05755[Unsupervised learning][Eigenstate phases of Matter]
- https://arxiv.org/abs/2004.14271[Universal approach][Unsupervised classification][Univariate data]
- https://arxiv.org/abs/2406.15004[Dislocation cartography][Representation][Unsupervised classification][Dislocation network][Unique fingerprint]
- https://arxiv.org/abs/2311.02442[Quantum transport][Network][Supervised classification]
- https://arxiv.org/abs/2006.12953[Unsupervised learning][Universal critical behavior][Intrinsic dimension]
- https://arxiv.org/abs/2010.14516[Unsupervised Learning][Non-Hermitian topological phase][Phys. Rev. Lett. 126, 240402 (2021)]
- https://arxiv.org/abs/2101.06892[Unsupervised learning][Ferroic variant][Atomically resolved STEM image]
- https://arxiv.org/abs/2102.11328[Observation][Complexity][Quantum state][Unsupervised learning]
- https://arxiv.org/abs/2103.00467[Unsupervised learning][Atomic environments][Simple features]
- https://arxiv.org/abs/2106.13485[Decoding][Conformal field theory][Supervised to unsupervised learning]
- https://arxiv.org/abs/2107.10468[Unsupervised learning][Topological phase diagram][Topological data analysis]
- https://arxiv.org/abs/2107.14311[Unsupervised learning-based structural analysis][Characteristic low-dimensional space][Local structure][Atomistic simulation]
- https://arxiv.org/abs/2109.06179[Unsupervised learning][Characterize][Heterogeneous][X-ray single particle imaging]
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- https://arxiv.org/abs/2112.11894[High dimensional fluctuation][Liquid water][Combining chemical intuition][Unsupervised learning]
- https://arxiv.org/abs/2112.13785[Experimental unsupervised learning][Non-Hermitian knotted phase][Solid-state spin]
- https://arxiv.org/abs/2201.02187[Density-of-states similarity descriptor][Unsupervised learning][Materials data]
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- https://arxiv.org/abs/2206.03871[Inorganic crystal structure prototype database][Unsupervised learning][Local atomic environment]
- https://arxiv.org/abs/2212.14813[Unsupervised learning][Structure detection][Plastically deformed crystal]
- https://arxiv.org/abs/2302.01465[Unsupervised learning][Representative local atomic arrangement][Molecular dynamics data]
- https://arxiv.org/abs/2308.01524[Unsupervised learning][Part similarity][Goal-guided accelerated experiment design][Metal additive manufacturing]
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- Fumitatsu Iwase, J. Phys. Soc. Jpn. 90, 094801 (2021)[Machine learning approach][Prediction][Nuclear quadrupole resonance frequency]
- Yu Hasegawa, Akihiro Haga, , Dousatsu Sakata, Yuki Kanazawa, Masahide Tominaga, Motoharu Sasaki, Toshikazu Imae, and Keiichi Nakagawa, J. Phys. Soc. Jpn. 90, 074801 (2021)[Estimation][X-ray energy spectrum][Cone-beam][Tomography scanner][Percentage depth dose Measurement][Machine learning approach]
- A. Sakamoto, K. Shiina and H. Mori, J. Phys. Soc. Jpn. 90, 065001 (2021)[Image preparation][Machine-learning analysis][Multiple-size spin system]
- Shogo Fukushima, Kohei Shimamura, Akihide Koura, and Fuyuki Shimojo, J. Phys. Soc. Jpn. 92, 054005 (2023)[Efficient training][Machine-learning interatomic potential][Artificial neural network][Estimating][Helmholtz free energy][Alkali metal]
- K. Fuchizaki, K. Nakamura and D. Hiroi, J. Phys. Soc. Jpn. 90, 055001 (2021)[Nonequilibrium relaxation scheme][Machine learning][Detecting][Phase transition]
- H. Araki, T. Yoshida and Y. Hatsugai, J. Phys. Soc. Jpn. 90, 053703 (2021)[Machine learning][Mirror skin effect][Disorder]
- N. Mamada, M. Mizumaki, I. Akai, T. Aonishi, J. Phys. Soc. Jpn. 90, 014705 (2021)[Obtaining underlying parameter][Magnetic domain pattern][Machine learning]
- Y. Nomura, J. Phys. Soc. Jpn. 91, 054709 (2022)[Investigating network parameter][Neural-network quantum state]
- Y. Nomura, J. Phys. Soc. Jpn. 89, 054706 (2020)[Machine learning][Quantum state][Extension][Fermion-Boson coupled system][Excited-state calculation][arXiv:2001.02106]
- Kazuya Shinjo, et al., J. Phys. Soc. Jpn. 88, 065001 (2019)[Machine learning phase diagram][Half-filled one-dimensional extended Hubbard model]
- Askery Canabarro, Samurai Brito, and Rafael Chaves, Phys. Rev. Lett. 122, 200401 (2019)[Machine Learning][Nonlocal correlation]
- M. Tsubaki and T. Mizoguchi, Phys. Rev. Lett. 125, 206401 (2020)[Quantum deep field][Data-driven wave function][Electron density generation][Atomization energy prediction][Extrapolation][Machine learning][arXiv:2011.07923]
- J. Timmermann, et al., Phys. Rev. Lett. 125, 206101 (2020)[IrO2 surface][Complexion][Machine learning][Surface investigation]
- Jose A. Garrido Torres, Paul C. Jennings, Martin H. Hansen, Jacob R. Boes, and Thomas Bligaard, Phys. Rev. Lett. 122, 156001 (2019)[Low-scaling algorithm][Nudged elastic band calculation][Surrogate machine learning model]
- Ryo Tamura, Jianbo Lin, and Tsuyoshi Miyazaki, J. Phys. Soc. Jpn. 88, 044601 (2019)[Machine learning force][Trained by Gaussian process][Liquid state][Transferability][Temperature][Pressure]
- Nobuo Nagashima, Masao Hayakawa, Hiroyuki Masuda, Kotobu Nagai, Materials Transactions, Vol.65, No.04, 428-433 (2024)[Estimating][S-N curve][Machine learning random forest method]
- Nobusuke Hasegawa, Shingo Hasegawa, Takafumi Kitaoka and Hiroyasu Ohtsu, Materials Transactions, Vol.60, No.05, 758 (2019)[Applicability][Neural network][Rock classification][Mountain tunnel]
- Takayuki Shiraiwa, Yuto Miyazawa and Manabu Enoki, Materials Transactions, Vol. 60, No.02, 189 (2019)[Prediction of fatigue strength][Steel][Linear regression][Neural network]
- Lucas Foppa, Thomas A.R. Purcell, Sergey V. Levchenko, Matthias Scheffler, and Luca M. Ghringhelli, Phys. Rev. Lett. 129, 055301 (2022)[Hierarchical symbolic regression][Identifying key physical parameter][Correlated][Bulk Properties of Perovskites]
- Kazuki Akiyama, Ilgoo Kang, Toshitake Kanno and Nozomu Uchida, Materials Transactions, Vol. 62, No.03, 461 (2021)[Prediction][Residual Mg content][Ladle][Product][Graphite spheroidizing treatment][Artificial neural network]
- Ivan Lobzenko, Tomohito Tsuru, Hideki Mori, Daisuke Matsunaka, Yoshinori Shiihara, Materials Transactions, Vol. 64, No.10, 2481 (2023)[Implementation][Atomic stress calculation][Artificial neural network potential]
- Parviz Kahhal, Hossein Ghorbani-Menghari, Hwi-Jun Kim, Hyunjoo Choi, Pil-Ryung Cha, Ji Hoon Kim, Materials Transactions, Vol. 64, No.11, 2648 (2023)[Metaheuristic optimization][Powder size distribution][Powder forming process][Multi-particle finite element method][Artificial neural network][Genetic algorithm]
- Qiang Du, Kjerstin Ellingsen, Mohammed M’Hamdi, Astrid Marthinsen, Knut O. Tveito, Materials Transactions, Vol. 64, No.02, p. 360-365 (2023)[Integration][Neural network][High throughput][Multi-scale simulation][Establishing][Digital twin][Aluminium billet DC-casting]
- Tomoki Hirosawa, Frank Schäfer, Hideaki Maebashi, Hiroyasu Matsuura, and Masao Ogata, J. Phys. Soc. Jpn. 91, 114603 (2022)[Data-Driven reconstruction][Spectral conductivity][Chemical potential][Thermoelectric transport properties]
- Ayaka Sakata, J. Phys. Soc. Jpn. 89, 084001 (2020)[Bayesian inference][Infected patient][Group testing][Prevalence estimation]
- Ryota Moriguchi, Satoshi Tsutsui, Shun Katakami, Kenji Nagata, Masaichiro Mizumaki, and Masato Okada, J. Phys. Soc. Jpn. 91, 104002 (2022)[Bayesian inference][Hamiltonian selection][Mössbauer spectroscopy]
- Yui Hayashi, Shun Katakami, Shigeo Kuwamoto, Kenji Nagata, Masaichiro Mizumaki, and Masato Okada, J. Phys. Soc. Jpn. 92, 094002 (2023)[Bayesian inference][Small-angle scattering data]
- Hiroki Tanaka and Ken Umeno, J. Phys. Soc. Jpn. 92, 113001 (2023)[Bayesian inference method][Large magnitude event][Spatiotemporal marked point process][Representing seismic activity]
- Hiroki Tanaka and Ken Umeno, J. Phys. Soc. Jpn. 93, 024001 (2024)[Bayesian updating][Time interval][Different magnitude threshold][Marked point process][Application][Synthetic seismic activity]
- K. Iwamitsu, M. Okada and I. Akqai, J. Phys. Soc. Jpn. 89, 104004 (2020)[Spectral decomposition][Component][Weaker][Noise intensity][Bayesian spectroscopy]
- H. Sakamoto, S. Katakami, K. Muto, K. Nagata, T. Arima, and M. Okada, J. Phys. Soc. Jpn. 89, 124002 (2020)[Bayesian parameter estimation][Dispersion relation spectra]
- Kenji Nagata, Rei Muraoka, Yoh-ichi Mototake, Takehiko Sasaki, and Masato Okada, J. Phys. Soc. Jpn. 88, 044003 (2019)[Bayesian spectral deconvolution][Poisson distribution][Bayesian measurement][Virtual measurement Analytics]
- K. Okajima, K. Nagata, and M. Okada, J. Phys. Soc. Jpn. 90, 034001 (2021)[Fast Bayesian deconvolution][Simple reversible jump move]
- M. Ukita, J. Phys. Soc. Jpn. 91, 064002 (2022) [Model design][Bayesian spectral deconvolution]
- Hajime Ueda, Shun Katakami, Shogo Yoshida, Takehide Koyama, Yusuke Nakai, Takeshi Mito, Masaichiro Mizumaki, and Masato Okada, J. Phys. Soc. Jpn. 92, 054002 (2023)[Bayesian approach][T1 analysis][NMR spectroscopy][Application][Solid state physics]
- Y. Yokoyama, T. Uozumi, K. Nagata, M. Okada, and M. Mizumaki, J. Phys. Soc. Jpn. 90, 034703 (2021)[Bayesian integration][Hamiltonian parameter][X-ray photoemission][Absorption spectroscopy]
- Rikuto Fukuma and Kozo Okada, J. Phys. Soc. Jpn. 90, 094709 (2021)[Theory of core level photoemission][Uranium intermetallic compound][Combined with Bayesian data analysis approach]
- Keisuke Morita, Yoshihiko Nishikawa, and Masayuki Ohzeki, J. Phys. Soc. Jpn. 92, 123801 (2023)[Random postprocessing][Combinatorial Bayesian optimization]
- Rei Nishimura, Shun Katakami, Kenji Nagata, Masaichiro Mizumaki, and Masato Okada, J. Phys. Soc. Jpn. 93, 034003 (2024)[Bayesian integration][Hamiltonian parameter][Crystal field]
- Yasuhiko Igarashi, et al., J. Phys. Soc. Jpn. 93, 074001 (2024)[Appropriate basis selection][Bayesian inference][Analyzing measured data reflecting photoelectron wave interference]
- Kota Ido, Yuichi Motoyama, Kazuyoshi Yoshimi, and Takahiro Misawa, J. Phys. Soc. Jpn. 92, 064702 (2023)[Data analysis][Ab initio effective Hamiltonian][Iron-based superconductor][Construction][Predictor][Superconducting critical temperature]
- Yuichi Yokoyama, Naruki Tsuji, Ichiro Akai, Kenji Nagata, Masato Okada, and Masaichiro Mizumaki, J. Phys. Soc. Jpn. 90, 094802 (2021)[Bayesian orbital decomposition][Determination of end condition][Magnetic Compton scattering]
- Kazunori Iwamitsu, et al., J. Phys. Soc. Jpn. 90, 104004 (2021)[Replica-exchange Monte Carlo method][Incorporating auto-tuning algorithm][Acceptance ratio][Effective Bayesian spectroscopy]
- T. Konno, J. Phys. Soc. Jpn. 89, 124006 (2020)[Deep-learning estimation][Band gap][Reading-periodic-table method][Periodic convolution layer]
- Dian Wu, Lei Wang, and Pan Zhang, Phys. Rev. Lett. 122, 080602 (2019)[Solving statistical mechanics][Variational autoregressive network]
- D. J. Chang, C. M. O’Leary, C. Su, D. A. Jacobs, S. Kahn, A. Zettl, J. Ciston, P. Ercius, and J. Miao, Phys. Rev. Lett. 130, 016101 (2023)[Deep-Learning][Electron diffractive imaging]
- Qianke Wang, Dawei Lyu, Jun Liu, and Jian Wang, Phys. Rev. Lett. 133, 140601 (2024)[Polarization][Orbital angular momentum][Encoded quantum Toffoli gate][Diffractive neural network]
- Pavlo Bilous, Adriana Palffy, and Florian Marquardt, Phys. Rev. Lett. 131, 133002 (2023)[Deep-learning approach][Atomic configuration interaction problem][Large basis sets]
- J. R. Moreno, G. Carleo, and A. Georges, Phys. Rev. Lett. 125, 076402 (2020)[Deep learning][Hohenberg-Kohn map][DFT]
- X. Liu, et al., Phys. Rev. Lett. 124, 113202 (2020)[Deep learning][Feynman's path integral][Strong-field time-dependent dynamics]
- Tomi Ohtsuki and Tomohiro Mano, Phys. Soc. Jpn. 89, 022001 (2020)[
Drawing phase diagram][Random quantum system][Deep learning][Wave function][arXiv:1909.09821]
- Or Sharir, Yoav Levine, Noam Wies, Giuseppe Carleo, and Amnon Shashua, Phys. Rev. Lett. 124, 020503 (2020)[Deep autoregressive model][Efficient variational simulation][Many-body quantum system][arXiv:1902.04057]
- Masahiko Demura, Materials Transactions, Vol.62 No.11 (2021) pp.1669-1672[Materials integration][Accelerating research][Development][Structural material]
- [http://www.jim.or.jp/journal/e/61/11/][Many papers of SIP-Materials Integration Projects]
- Junhyub Jeon, Namhyuk Seo, Jae-Gil Jung, Seung Bae Son, Seok-Jae Lee, Materials Transactions, Vol.64 No.09 (2023) pp.2196-2201[Analysis][Prediction mechanisms][Feature importance][Martensite start temperature][Alloy steel][Explainable artificial intelligence]
- Yuichi Okazaki, Yasuaki Tokudome, Shunsuke Yagi, Ikuya Yamada, Materials Transactions, Vol.64 No.09 (2023) pp.2082-2087[High-throughput screening][(La,Sr)(Fe,Co)O3 perovskite][Oxygen evolution reaction catalysis]
- Megumi Higashi, Hidekazu Ikeno, Materials Transactions, Vol.64 No.09 (2023) pp.2179-2184[Extraction][Local structure information][X-ray absorption near-edge structure][Machine learning approach]
- Junhyub Jeon, Yoonje Sung, Namhyuk Seo, Jae-Gil Jung, Seung Bae Son, Seok-Jae Lee, Materials Transactions, Vol.64 No.09 (2023) pp.2214-2218[Machine learning model][Prediction mechanisms][Bainite start temperature][Low alloy steel]
- Qi Kong, Yasushi Shibuta, Materials Transactions, Vol.64 No.06 (2023) pp.1241-1249[High-precision prediction][Thermal conductivity][Metal][Molecular dynamics simulation][Combination][Machine learning approach]
- Zixiang Qiu, Kenjiro Sugio and Gen Sasaki, Materials Transactions, Vol.62 No.06 (2021) pp.719-725[Classification][Microstructure][Al-Si casting alloy][Different cooling rate][Machine learning technique]
- Yudai Iwamizu, Kota Suzuki, Naoki Matsui, Masaaki Hirayama, Ryoji Kanno, Materials Transactions, Vol.64 No.1 (2023) pp.287-295[Search][Lithium ion conducting oxide][Predicted ionic conductivity][Machine learning]
- Jungjoon Kim, Dongchan Min, Suwon Park, Junhyub Jeon, Seok-Jae Lee, Youngkyun Kim, Hwi-Jun Kim, Youngjin Kim, Hyunjoo Choi, Materials Transactions, Vol.63 No.10 (2022) pp.1304-1309[Optimization][Densification behavior][Soft magnetic powder][Discrete element method][Machine learning]
- Junhyub Jeon, Namhyuk Seo, Jae-Gil Jung, Seung Bae Son, Seok-Jae Lee, Materials Transactions, Vol.63 No.10 (2022) pp.1369-1374[Machine learning prediction][Cementite precipitation][Austenite][Low-alloy steel]
- Qi Kong, Yasushi Shibuta, Materials Transactions, Vol.65 No.7 (2024) pp.790-797[Advancing thermal conductivity prediction][Metallic material][Integrating molecular dynamics simulation][Machine learning]
- Kyohei Hayakawa, Isao Matsui, Yuichi Sekine, Takaharu Maeguchi, Materials Transactions, Vol.65 No.8 (2024) pp.977-986[Machine learning][Predict][Effect of stress][Iron loss][Frequency dependence][Non-oriented electrical steel]
- Tatsuya Maemura, Hidenori Terasaki, Kazumasa Tsutsui, Kyohei Uto, Shogo Hiramatsu, Kotaro Hayashi, Koji Moriguchi and Shigekazu Morito, Materials Transactions, Vol.61 No.08 (2020) pp.1584-1592[Interpretability][Deep learning classification][Low-carbon steel microstructure]
- Masato Shirai and Hiroshi Yamada, Materials Transactions, Vol.61 No.01 (2020) pp.176-180[Mechanical properties prediction][Gray cast iron][Trace Element][Deep Learning]
- Ye Li, Materials Transactions, Vol.64 No.11, 2547 (2023)[Deep learning][Classifying structure][Crystal system][Pure metal]
- Kohta Koenuma, Akinori Yamanaka, Ikumu Watanabe and Toshihiko Kuwabara, Materials Transactions, Vol.61 No.12 (2020) pp.2276-2283[Estimation][Texture-dependent stress-strain curve][r-Value][Aluminum alloy sheet][Deep learning]
- Guancen Liu, Zhiwei Zheng, Rusheng Zhao, Xuezheng Yue, Materials Transactions, Vol.65 No.3 (2024) pp. 308-317[Horseshoe lattice property-structure][Inverse design][Deep learning]
- Takayuki Shiraiwa, Koki Yasuda, Fabien Briffod, Mark Jhon, Fergyanto Gunawan, Rahul Sahay, Nagarajan Raghavan, Arief S. Budiman, Manabu Enoki, Materials Transactions, Vol.65 No.6 (2024) pp. 677-686[Materials informatics approach][Cu/Nb nanolaminate microstructure correlation][Yield strength][Electrical conductivity]
- Yoav Levine, Or Sharir, Nadav Cohen, and Amnon Shashua, Phys. Rev. Lett. 122, 065301 (2019)[Quantum entanglement][Deep learning architecture]
- Ken-Ichi Aoki Tatsuhiro Fujita and Tamao Kobayashi, J. Phys. Soc. Jpn. 88, 054002 (2019) [Logical reasoning][Revealing][Critical temperature][Deep learning][Configuration ensemble][Statistical system]
- Zhanwei Liu, Shuo Yan, Haigang Liu, and Xianfeng Chen, Phys. Rev. Lett. 123, 183902 (2019)[Superhigh-resolution recognition][Optical vortex mode][Assisted][Deep-learning method]
- M. Bürkle, et al., Phys. Rev. Lett. 126, 177701 (2021)[Deep-learning][First-principles transport simulation]
- Maria Schuld and Nathan Killoran, Phys. Rev. Lett. 122, 040504 (2019)[Quantum machine learning][Feature Hilbert space]
- Tobias Haug and M.窶%@S. Kim, Phys. Rev. Lett. 133, 050603 (2024)[Generalization][Quantum machine learning model][Quantum Fisher Information metric]
- Rodrigo A. Vargas-Hernandez, John Sous, Mona Berciu, and Roman V. Krems, Phys. Rev. Lett. 121, 255702 (2018)[Extrapolating][Quantum observables][Machine learning][Inferring multiple phase transition][Single phase][https://arxiv.org/abs/1803.08195]
- H.-Y. Huang, R. Kueng and J. Preskill, Phys. Rev. Lett. 126, 190505 (2021)[Information-theoretic bound][Quantum advantage][Machine learning]
- Paul Z. Hanakata, Ekin D. Cubuk, David K. Campbell, and Harold S. Park, Phys. Rev. Lett. 121, 255304 (2018)[Accelerated search and design][Stretchable graphene kirigami][Machine learning]
- Z. Liu and M. Tegmark, Phys. Rev. Lett. 126, 180604 (2021)[Machine learning][Conservation law][Trajectry]
- Ye-Fei Li and Zhi-Pan Liu, Phys. Rev. Lett. 128, 226102 (2022)[Smallest stable Si/SiO2 interface][Suppresses quantum tunneling][Machine-learning-based global search]
- Keshav Srinivasan, Nolan Coble, Joy Hamlin, Thomas Antonsen, Edward Ott, and Michelle Girvan, Phys. Rev. Lett. 128, 164101 (2022)[Parallel machine learning][Forecasting][Dynamics of complex network]
- Takahiro Goto, Quoc Hoan Tran, and Kohei Nakajima, Phys. Rev. Lett. 127, 090506 (2021)[Universal approximation property][Quantum machine learning model][Quantum-enhanced feature space]
- Hao Tang, Boning Li, Guoqing Wang, Haowei Xu, Changhao Li, Ariel Barr, Paola Cappellaro, and Ju iL, Phys. Rev. Lett. 130, 150602 (2023)[Communication-efficient quantum algorithm][Distributed machine learning]
- Soham Chattopadhyay and Dallas R. Trinkle, Phys. Rev. Lett. 132, 186301 (2024)[Contributions to Diffusion][Complex material][Quantified][Machine learning]
- Andrew H. Proppe, Kin Long Kelvin Lee, Alexander E.K. Kaplan, Matthias Ginterseder, Chantalle J. Krajewska, and Moungi G. Bawendi, Phys. Rev. Lett. 131, 053603 (2023)[Time-resolved line shape][Single quantum emitter][Machine learned photon correlation]
- M.F. Kasim and S.M. Vinko, Phys. Rev. Lett. 127, 126403 (2021)[Learning][Exchange-correlation functional][Nature][Fully differentiable DFT]
- Isao Ohkubo, Zhufeng Hou, Jiyeon N. Lee, Takashi Aizawa, Mikk Lippmaa, Toyohiro Chikyow, Koji Tsuda, and Takao Mori, Materials Today Physics 16 (2020) 100296 [Realization][Closed-loop optimization][Epitaxial titanium nitride thin-film growth][Machine learning]
- Yuqing Jin, Takahiro Kozawa and Takao Tamura, Jpn. J. Appl. Phys. 60, 076509 (2021)[Analysis][Mitigating factor][Line edge roughness][Electron beam lithography][Machine learning]
- Sena Suzuki and Jun Kondoh, Jpn. J. Appl. Phys. 60, SDDC09 (2021)[Cantilever damage evaluation][Impedance-loaded SAW sensor][Continuous wavelet analysis][Machine learning]
- N. Tanaka, et al., Jpn. J. Appl. Phys. 60 066503 (2021)[Analysis][Dissolution kinetics][Narrow polydispersity][Alkaline aqueous solution][Machine learning]
- Hongpeng Wang, Shangce Gao, Michiya Mozumi, Masaaki Omura, Ryo Nagaoka and Hideyuki Hasegawa, Jpn. J. Appl. Phys. 60, SDDE21 (2021)[Preliminary investigation][Clutter filtering][Deep learning]
- K. Ando, et al., Jpn. J. Appl. Phys. 59 SKKE06 (2020)[Speckle reduction][Medical ultrasound image][Deep learning][Fully convolutional network]
- N. Takahashi, Y. Liu and C. Kaneta, Jpn. J. Appl. Phys. 59 051005 (2020)[Materials informatics approach][Design][Si/Ge layered][Nanostructure][Low thermal conductivity]
- Y. Kurokawa, T. Aoki, M. Kozuma, H. Kimura, T. Kanemura, Y. Ando and S. Yamazaki, Jpn. J. Appl. Phys. 59 SGGB03 (2020)[Hybrid structured analog multiplier][Vector-by-matrix multiplier[Neural network]
- Y.-H. Lin, et al., Jpn. J. Appl. Phys. 59 SGGB15 (2020)[Impact][Solution][Nonvolatile-memory-induced][Weight error][Computing-in-memory][Neural network system]
- X. Wu, et al., Jpn. J. Appl. Phys. 60 067001 (2021)[Rapid][Accurate][Identification][Colon cancer][Raman spectroscopy][Convolutional neural network]
- S. Ohno, et al., Jpn. J. Appl. Phys. 59 SGGE04 (2020)[Microring resonator][Crossbar array][Deep learning accelerator]
- D. Suzuki, T. Oka and T. Hanyu, J. Appl. Phys. 60 SBBB07 (2021)[Design][Energy-efficient binarized convolutional neural network accelerator][Nonvolatile field-programmable gate array][Only-once-write shifting]
- M. Yamakawa, et al., Jpn. J. Appl. Phys. 59 SKKE09 (2020)[Optimal cropping][Input image][Convolutional neural network][Ultrasonic diagnosis][Liver tumor]
- Kyungchan Son, Wooyoung Jeong, Wonseok Jeon and Hyunseok Yang, Jpn. J. Appl. Phys. 57 09SB02 (2018)[Autofocusing algorithm][Digital holographic imaging system][Convolutional neural network]
- Yutaro Katano, Tetsuhiko Muroi, Nobuhiro Kinoshita, Norihiko Ishii and Naoto Hayashi, Jpn. J. Appl. Phys. 57 09SC01 (2018)[Data demodulation][Convolutional neural network][Holographic data storage]
- Ya-Dong Wu, Yan Zhu, Ge Bai, Yuexuan Wang, and Giulio Chiribella, Phys. Rev. Lett. 130, 210601 (2023)[Quantum similarity testing][Convolutional neural network]
- Yu-Jie Liu, Adam Smith, Michael Knap, and Frank Pollmann, Phys. Rev. Lett. 130, 220603 (2023)[Model-independent learning][Quantum phase][Matter][Quantum convolutional neural network]
- Volker L. Deringer, Chris J. Pickard, and Gábor Csányi, Phys. Rev. Lett. 120, 156001(2018)[Data-driven learning][Total and local energies][Elemental boron]
- P. Rotondo, M. Pastore and M. Gherardi, Phys. Rev. Lett. 125, 120601(2020)[Beyond the strage capacity][Data-driven][Satisfiability transition]
- R. A. Mansbach and A. L. Ferguson, J. Chem. Phys. 142, 105101(2015)[Machine learning][Free energy surface][Single molecule]
- F. A. Faber, A. Lindmaa, O. A. von Lilienfeld and R. Armiento, Phys. Rev. Lett., Vol. 117, No. 14, 135502(2016)[Machine learning energy][2 Million elpasolite (ABC2D6) crystals]
- Hiromi Oda, Shin Kiyohara, Koji Tsuda and Teruyasu Mizoguchi, "Transfer Learning to Accelerate Interface Structure Searches", J. Phys. Soc. Jpn. 86, 123601 (2017).
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- (参考サイト)
- BiGmax(MAX PLANCK RESEARCH NETWORK)
- Berlin Big Data Center
- http://hauleweb.rutgers.edu/database_w2k/(DFT & DMFT Materials Database、Rutgers大学)
- Machine learning for atomistic systems(Matthias Rupp先生による、機械学習のページ)
- OQMD(Open Quantum Materials Database)
- Computational Electronic Structure Database(CompES-X, NIMS)
- NIST(*)(National Institute of Standards and Technology)
- 数理材料科学コミュニティ
- ↑協働例:物質の階層構造をとりいれたマテリアルズ・インフォマティクス(png画像、東北大学WPI-AIMR)
- 科学技術情報プラットフォーム(*)(JST[科学技術振興機構]のページ、ビッグデータ関連)
↑”わが国におけるデータシェアリングのあり方に関する”提言あり。
- [その他いろいろ][小目次]
- Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach(Computational Chemistry Highlights): 関連文献:http://arxiv.org/abs/1503.04987
- Automatic chemical design using a data-driven continuous representation of molecules(Computational Chemistry Highlights)
- A Density Functional Tight Binding Layer for Deep Learning of Chemical Hamiltonians(Computational Chemistry Highlights): 関連文献:https://arxiv.org/abs/1808.04526
- DeepSMILES: An adaptation of SMILES for use in machine-learning of chemical structures(Computational Chemistry Highlights)
- Artificial Intelligence Assists Discovery of Reaction Coordinates and Mechanisms from Molecular Dynamics Simulations(Computational Chemistry Highlights)
- A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules(Computational Chemistry Highlights)
- Physical machine learning outperforms "human learning" in Quantum Chemistry(Computational Chemistry Highlights)
- Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning(Computational Chemistry Highlights)
- Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions(Computational Chemistry Highlights)
- Schrödinger-ANI: An Eight-Element Neural Network InteractionPotential with Greatly Expanded Coverage of Druglike Chemical Space(Computational Chemistry Highlights)
- Open Graph Benchmark: Datasets for Machine Learning on Graphs(Computational Chemistry Highlights)
- Learning Molecular Representations for Medicinal Chemistry(Computational Chemistry Highlights)
- What Does the Machine Learn? Knowledge Representations of Chemical Reactivity(Computational Chemistry Highlights)
- OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features(Computational Chemistry Highlights)
- Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors(Computational Chemistry Highlights)
- Identifying domains of applicability of machine learning models for materials science(Computational Chemistry Highlights)
- Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations(Computational Chemistry Highlights)
- Accelerating High-Throughput Virtual Screening Through Molecular Pool-Based Active Learning(Computational Chemistry Highlights)
- Uncertainty Quantification Using Neural Networks for Molecular Property Prediction(Computational Chemistry Highlights)
- Leveraging Uncertainty in Machine LearningAccelerates Biological Discovery and Design(Computational Chemistry Highlights)
- Using attribution to decode binding mechanism in neural network models for chemistry(Computational Chemistry Highlights)
- Bayesian optimization of nanoporous materials(Computational Chemistry Highlights)
- Evidential Deep Learning for Guided Molecular Property Prediction and Discovery(Computational Chemistry Highlights)
- Explaining and avoiding failures modes in goal-directed generation(Computational Chemistry Highlights)
- Pushing the frontiers of density functionals by solving the fractional electron problem(Computational Chemistry Highlights)
- Machine learning potentials always extrapolate, it does not matter.(Computational Chemistry Highlights)
- Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki−Miyaura Coupling(Computational Chemistry Highlights)
- Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search(Computational Chemistry Highlights)
- Deep Learning Metal Complex Properties with Natural Quantum Graphs(Computational Chemistry Highlights)
- Semiempirical Hamiltonians learned from data can have accuracy comparable to Density Functional Theory(Computational Chemistry Highlights)
- Quantum Chemical Data Generation as Fill-In for Reliability Enhancement of Machine-Learning Reaction and Retrosynthesis Planning(Computational Chemistry Highlights)
- Machine-Learning-Guided Discovery of Electrochemical Reactions(Computational Chemistry Highlights)
- Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability(Computational Chemistry Highlights)
- Real-World Molecular Out-Of-Distribution: Specification and Investigation(Computational Chemistry Highlights)
- Accelerated dinuclear palladium catalyst identification through unsupervised machine learning(Computational Chemistry Highlights)
- Ranking Pareto optimal solutions based on projection free energy(Computational Chemistry Highlights)
- Few-Shot Learning for Low-Data Drug Discovery(Computational Chemistry Highlights)
- 日立のプレスリリース記事:「KEK向けに、磁性材料の磁気構造に関するシミュレーションデータや量子ビーム実験データを解析・可視化するシステムの開発を支援 - マテリアルズ・インフォマティクスに基づく材料開発の効率化に貢献 - 」
- (情報)”NEC、ビッグデータの高精度な予測分析に必要な期間を従来比1/3に短縮する「特徴量自動設計技術」を開発”(NECのプレスリリース記事(*))
- (マイナビニュース記事1)「機械学習によって金属ガラス材料の探索を高速化 - 米SLACなどが発表 - 」←詳細は、当該マイナビニュース記事参照。
- (プレスリリース情報1)「希少元素を使わずに赤く光る新窒化物半導体を発見」←東工大のプレスリリース記事
- (プレスリリース情報2)「「京」でペロブスカイト太陽電池の新材料候補を発見―膨大な数から適切な材料を効率よく探し出す―」←理研のプレスリリース記事
- (プレスリリース情報3)「新材料開発の期間・コストの削減を支援する「材料開発ソリューション」を提供開始 AIを活用したマテリアルズ・インフォマティクスに基づくデータ分析支援サービスなどを提供」←日立のプレスリリース記事
- (プレスリリース情報4)「人工知能が「繰り返し成長すること」で計算コストを1/3600に削減 〜 界面構造を高速に決定し、高性能な物質開発を加速 〜 」←東大生産研のプレスリリース記事
- (プレスリリース情報5)「Artificial intelligence aids materials fabrication」←MITのプレスリリース(ニュース)記事[MIT News]
- (プレスリリース情報6)「人工知能(AI)で触媒反応の収率を予測 - キャタリストインフォマティクスで触媒の発見に道 -」←産総研のプレスリリース記事
- (プレスリリース情報7)「材料設計におけるAIの有用性を実証:高イオン伝導率を有する全固体リチウムイオン電池用固体電解質の開発を効率化」←富士通のプレスリリース記事
- (プレスリリース情報8)「高分子太陽電池、人工知能で性能予測 〜1,200個の実験データから有効性を実証〜 」←科学技術振興機構(JST)のプレスリリース記事
- (プレスリリース情報9)「2種類の深層学習手法の組み合わせで薬剤とタンパク質の相互作用を予測 - 高速で高精度な予測と相互作用部位の特定・可視化による検証を実現 - 」←産総研のプレスリリース記事
- (プレスリリース情報10)「人工知能でタンパク質を自動設計 - 様々な機能性タンパク質開発の加速に期待 - 」←産総研のプレスリリース記事
- (プレスリリース情報11)「人工知能が専門家の約2万倍の速さでスペクトルを解釈 〜知識や職人技なしで、物質の性質を明らかに〜」←東大生産研のプレスリリース記事
- (プレスリリース情報12)「分子構造を設定するだけで物性値を高速・高精度で予測 - 時間のかかる理論計算を1万倍以上高速化し材料開発のプロセスを加速 - 」←産総研のプレスリリース記事
- (プレスリリース情報13)「機械学習の関数を用いて量子状態を表現する方法を提唱 - 機械学習の手法を用いて量子多体系の性質に迫る -」←東大工学部のプレスリリース記事
- (プレスリリース情報14)「組合せ最適化問題を効率的に解くための新しいアナログニューラルネットワーク」←東大生産研のプレスリリース記事
- (プレスリリース情報15)「理論計算や専門知識いらず!人工知能がスペクトルから物質の機能と構造を定量 〜物質開発の加速に期待〜 」←東大生産研のプレスリリース記事
- (プレスリリース情報16)「機械学習で新しい超伝導物質を探索する - 21世紀版マティアス則の構築 - 」←九州工業大学のプレスリリース記事
- (プレスリリース情報16)「バイオエタノールからブタジエンを生成する世界最高の生産性を有する触媒システムを短期間で開発」←産総研のプレスリリース記事
- (プレスリリース情報17)「化学構造を手掛かりにしたデータサイエンスの手法で、天然物化合物の生合成経路の予測に成功! 有機分子の特性や薬剤設計の研究への活用期待」←奈良先端科学技術大学院大学のプレスリリース記事
- (プレスリリース情報18)「ディープラーニングモデルの新たな軽量化技術を開発 - 高度なAIの小規模・省電力運用に期待、データ利活用社会の実現に貢献 - 」←NEDOのプレスリリース記事
- (プレスリリース情報19)「NECと東北大学、「開発者が解釈可能なマテリアルズ ・インフォマティクス」で特性向上の主要因を抽出する手法を開発」←東北大学のプレスリリース記事
- (プレスリリース情報20)「新物質の合成条件を効率よく推薦する手法を開発」←京都大学のプレスリリース記事
- (プレスリリース情報21)「機械学習を活用したゼオライト合成の理解〜ほしい材料を思いのままに。究極の材料合成を目指して〜」←東京大学のプレスリリース記事
- (プレスリリース情報22)「AIを活用し、フレキシブル透明フィルム開発の実験回数を1/25以下に大幅低減 - 相反する複数の要求特性がある機能性材料開発への応用展開に期待 -」←NEDOのプレスリリース記事
- (プレスリリース情報23)「電子状態が変化する前の姿から、変化後の姿をAIが正確に予想 〜電子の励起状態を高速で計算、構造解析のアクセルに〜 」←東大生産研のプレスリリース記事
- (プレスリリース情報24)「AIに電子の物理を学習させる方法を開発」←東大物性研のプレスリリース記事
- (プレスリリース情報25)「最適化したナノ構造により結晶性材料の熱伝導率を最小に 〜MIを駆使して熱機能材料の開発へ応用期待〜 」←科学技術振興機構(JST)のプレスリリース記事
- (プレスリリース情報26)「IGZOと不揮発性メモリを三次元集積した新デバイスの開発に成功 〜ディープラーニングの高効率ハードウェア化へ期待〜 」←科学技術振興機構(JST)のプレスリリース記事
- (プレスリリース情報27)「大阪大学、兵庫県立大学、兵庫県による「関西におけるマテリアルズ・インフォマティクス推進にかかる連携・協力に関する覚書」の締結」←大阪大学産業科学研究所のプレスリリース記事
- (プレスリリース情報28)「光でプラスチックの劣化が診断可能に!? - 近赤外光と機械学習による材料診断 - 」←産総研のプレスリリース記事
- (プレスリリース情報29)「単一のAIに多彩な材料データを学習させる手法を開発」←早稲田大学のプレスリリース記事
- (プレスリリース情報30)「AIを使い生体材料(バイオマテリアル)の設計に成功 - 機械学習で生体分子の吸着を予測し、材料を高速スクリーニング - 」←東工大のプレスリリース記事
- (プレスリリース情報31)「量子化学理論とAI解析を組み合わせた電子状態インフォマティクス技術開発により温室効果ガスを効率的に吸収するイオン液体の混合方法を発見」←中央大のプレスリリース記事
- (プレスリリース情報32)「専門知識と機械学習を融合した最適化手法 ―最適な成膜条件により生成効率を約2倍に―」←理研のプレスリリース記事
- (プレスリリース情報33)「次世代有機ELの高効率化・長寿命化に成功 〜マテリアルズ・インフォマティクスで開発期間を短縮〜 」←科学技術振興機構(JST)のプレスリリース記事
- (プレスリリース情報34)「量子物理学の理論や波動関数に基づく新たな深層学習技術を開発 - 学習データにはない、分子構造が大きく異なる未知化合物に対する物性の外挿予測が可能 - (発表主体:産総研)」←東大生産研のプレスリリース記事
- (プレスリリース情報35)「自律的に物質探索を進めるロボットシステムを開発 - 物質・材料研究開発の進め方について革新を起こす - 」←東京工業大学のプレスリリース記事
- (プレスリリース情報36)「X線回折パターンからの対称性予測における知識発見 〜熟練者の勘・コツの定式化に成功〜 」←科学技術振興機構(JST)のプレスリリース記事
- (プレスリリース情報37)「ハイスループット実験と触媒インフォマティクスが実現するゼロからの触媒設計」←北陸先端科学技術大学院大学のプレスリリース記事
- (プレスリリース情報38)「人工知能を利用して磁石の磁気パラメータの推定に成功 - スピントロニクスの研究を人工知能で効率化 - 」←東大 理学系研究科・理学部のプレスリリース記事
- (プレスリリース情報39)「優れた高分子太陽電池材料を見いだす人工知能 - 仮想の20万種類から選び合成した新材料で有効性を実証 - 」←大阪大学のプレスリリース記事
- (プレスリリース情報40)「計算シミュレーションとAIを連携させ、仮想実験環境を構築 - 材料ビッグデータの創出と、それを用いるAI材料設計へ - 」←産総研のプレスリリース記事
- (プレスリリース情報41)「失敗は成功の母!創薬AIの精度向上 - 失敗例を1,000倍学ばせると誤分類が100分の1以下に - 」←関西医科大学のプレスリリース記事(PDF形式ページ)
- (プレスリリース情報42)「結晶の世界をのぞくニューラルネットワーク ―固体系のミクロな量子多体物性に迫る―」←理研のプレスリリース記事
- (プレスリリース情報43)「Snを添加したIGZO材料を用いた三次元集積メモリデバイスを開発 〜機械学習ハードウェアの高エネルギー効率化へ期待〜 」←東大生産研のプレスリリース記事
- (プレスリリース情報44)「触媒活性理論の実証に前進 ―実験、数理、機械学習の融合による非平衡触媒活性の解析―」←理研のプレスリリース記事
- (プレスリリース情報45)「リチウムイオン電池の高容量化に向けたデータ科学的手法による物質探索」←北陸先端科学技術大学院大学のプレスリリース記事
- (プレスリリース情報46)「機械学習で準結晶を形成する化学組成を同定
準結晶の安定化メカニズムの解明に向けた第一歩」←東京理科大学のプレスリリース記事
- (プレスリリース情報47)「結合前の情報だけで、結合後の性質を高精度に予測 - 化学反応や触媒の予測への応用に期待 - 」←東大生産研のプレスリリース記事
- (プレスリリース情報48)「限定性・偏向性のあるデータから新材料を推薦するシステムを開発 - 証拠理論を用いたシステムを開発し、新規合金薄膜材料合成で実証 - 」←北陸先端科学技術大学院大学のプレスリリース記事
- (プレスリリース情報49)「ベイズ推定を用いた新たな電子構造の解析法を開発 - トポロジカル絶縁体などを巡る数々の論争の決着へ - 」←産総研のプレスリリース記事
- (プレスリリース情報50)「触媒遺伝子「触媒シークエンシング」を発見 〜触媒インフォマティクスを駆使した新しい触媒開発に成功〜」←科学技術振興機構(JST)のプレスリリース記事
- (プレスリリース情報51)「AIモデルの開発により、たった1回の実験で新規プロトン伝導性電解質を発見! 〜中温動作燃料電池に用いる電解質材料の開発加速化に期待〜」←九州大学のプレスリリース記事
- (プレスリリース情報52)「熱と量子の揺らぎを発現する深層学習モデルを発見 - 「富岳」などにより自然界の根源的なシミュレーションが加速 -」←理研のプレスリリース記事
- (プレスリリース情報53)「機械学習手法により物理の難問「量子スピン液体」を解明 - スーパーコンピュータ「富岳」も用いた最先端の計算により実現 -」←理研のプレスリリース記事
- (プレスリリース情報54)「AIが生成した材料の構造画像を用い、物性を予測する技術を開発 - 材料の選定から加工・評価までを高速・高精度に再現、材料開発を加速 -」←NEDOのプレスリリース記事
- (プレスリリース情報55)「触媒ビッグデータから「触媒世界地図」を描写 〜ブラックボックス化していた触媒設計をひもとく〜」←科学技術振興機構(JST)のプレスリリース記事
- (プレスリリース情報56)「機械学習によるヨウ素の結合エネルギーの予測 従来法の1.3億倍のスピードでの予測に成功」←千葉大学のプレスリリース記事
- (プレスリリース情報57)「スペクトルから思いもかけない物性をAIが予測」←東大生産研のプレスリリース記事
- (プレスリリース情報58)「人工知能により材料の構造画像を生成し、物性を予測する技術を開発 - AI技術で扱える材料を広げ、材料開発加速へ - 」←産総研のプレスリリース記事
- (ニュース情報59)「データ駆動型材料設計技術利用推進コンソーシアムの設立に向けて - 高度なデータ解析技術が拓く新たな材料開発の世界へ - 」←産総研のプレスリリース記事、参考サイト:データ駆動型材料設計技術利用推進コンソーシアム
- (プレスリリース情報60)「ベイズ推定:電子状態解析の新手法を開発 - ベイズ理論を用いてトポロジカル絶縁体におけるディラック電子質量の解析に成功 - 」←東北大学(材料科学高等研究所)のプレスリリース記事
- (プレスリリース情報61)「機械学習を用いてタンパク質立体構造を評価する構造生物学AI技術を構築」←横浜市立大学のプレスリリース記事
- (プレスリリース情報62)「ロボット実験×AIによる燃料電池のものづくり研究開発法の革新 〜粉体成膜プロセスインフォマティクスにより3万候補から40回で新しい最適解の発見に成功〜」←科学技術振興機構(JST)のプレスリリース記事
- (プレスリリース情報63)「スーパーコンピュータ「富岳」による大規模物性データの自動創出 - 不規則系磁性材料におけるビッグデータの実現へ - 」←東大物性研のプレスリリース記事
- (プレスリリース情報64)「有機固体で実現する量子スピン液体の特異な性質を解明 人工ニューラルネットワーク第一原理計算による量子物質設計へ」←東大物性研のプレスリリース記事
- (プレスリリース情報65)「大量の実画像データの収集が不要なAIを開発 - 数式からAIが自動学習、人の判断を経た学習と同程度以上の認識精度を実現 - 」←産総研のプレスリリース記事
- (プレスリリース情報66)「データサイエンスを活用し、物質表面原子構造を高精度で自動解析 〜専門知識や熟練した技能は不要、材料開発の加速に期待〜」←東京理科大学のプレスリリース記事
- (プレスリリース情報67)「複数のAIを活用し、複雑な材料データからさまざまな機能を予測する技術を開発 - 配合条件の選定から成形加工・評価までの材料開発を大幅に加速 -」←NEDOのプレスリリース記事
- (プレスリリース情報68)「計算×情報×実験により 人間の経験則を超えた磁性材料の創製に成功 〜未踏物質の発見をアシスト〜」←東京理科大学のプレスリリース記事
- (プレスリリース情報69)「厳密な電子状態計算 × 機械学習ポテンシャル:高圧水素における液体-液体相転移の研究」←北陸先端科学技術大学院大学のプレスリリース記事
- (プレスリリース情報70)「高分子固体電解質をAIによる機械学習で自動設計する新たな手法を開発」←早稲田大学のプレスリリース記事
- (プレスリリース情報71)「壊れにくい窒化ケイ素セラミックスをAIが予測 - セラミックス材料開発の加速に貢献 - 」←産総研のプレスリリース記事
- (プレスリリース情報72)「電子状態インフォマティクス・合成・精密測定の三位一体研究によりCO2 物理吸収液の機能最適化法を確立」←中央大のプレスリリース記事
- (プレスリリース情報73)「深層学習でガラスに眠る未来を掘り起こす 原子同士の動き方の関係まで理解するグラフニューラルネットワーク」←東京大学のプレスリリース記事
- (プレスリリース情報74)「たった一部の情報から、すべての電子構造を決定 -- 原子一つ一つの全電子構造を計測する新手法の開発に、大きな前進 -- 」←東大生産研のプレスリリース記事
- (プレスリリース情報75)「電子のスピンを用いた人工ニューラルネットワークの 新しい動作原理を発見 - AIハードウェア実現に向けたノイズに強い超大規模並列計算が可能に -」←東大工学部のプレスリリース記事
- (プレスリリース情報76)「深層学習でナノ粒子評価の長年の課題を解決 - ブラウン運動の軌跡からナノ粒子の形状を識別 -」←東大工学部のプレスリリース記事
- (プレスリリース情報77)「理論計算と機械学習により無機材料表面の性質を高精度かつ網羅的に予測 - 光触媒材料などの探索や電子・光電子デバイスの設計を支援 - 」←東北大学(金属材料研究所)のプレスリリース記事
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