Alex Ganose, Hrushikesh Sahasrabuddhe, Mark Asta, Kevin Beck, Tathagata Biswas, Alexander Bonkowski, Joana Bustamante, Xin Chen, Yuan Chiang, Daryl Chrzan, Jacob Clary, Orion Cohen, Christina Ertural, Janine George, Max Gallant, Janine George, Sophie Gerits, Rhys Goodall, Rishabh Guha, Geoffroy Hautier, Matthew Horton, Aaron Kaplan, Ryan Kingsbury, Matthew Kuner, Bryant Li, Xavier Linn, Matthew McDermott, Rohith Srinivaas Mohanakrishnan, Aakash Naik, Jeffrey Neaton, Kristin Persson, Guido Petretto, Thomas Purcell, Francesco Ricci, Benjamin Rich, Janosh Riebesell, Gian-Marco Rignanese, Andrew Rosen, Matthias Scheffler, Jonathan Schmidt, Jimmy-Xuan Shen, Andrei Sobolev, Ravishankar Sundararaman, Cooper Tezak, Victor Trinquet, Joel Varley, Derek Vigil-Fowler, Duo Wang, David Waroquiers, Mingjian Wen, Han Yang, Hui Zheng, Jiongzhi Zheng, Zhuoying Zhu, Anubhav Jain
- High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2’s improved usability and extensibility can reduceHigh-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2’s improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.…


Metadaten| Autor*innen: | Alex Ganose, Hrushikesh Sahasrabuddhe, Mark Asta, Kevin Beck, Tathagata Biswas, Alexander Bonkowski, Joana Bustamante, Xin Chen, Yuan Chiang, Daryl Chrzan, Jacob Clary, Orion Cohen, Christina Ertural, Janine GeorgeORCiD, Max Gallant, Janine GeorgeORCiD, Sophie Gerits, Rhys Goodall, Rishabh Guha, Geoffroy Hautier, Matthew Horton, Aaron Kaplan, Ryan Kingsbury, Matthew Kuner, Bryant Li, Xavier Linn, Matthew McDermott, Rohith Srinivaas Mohanakrishnan, Aakash NaikORCiD, Jeffrey Neaton, Kristin Persson, Guido Petretto, Thomas Purcell, Francesco Ricci, Benjamin Rich, Janosh Riebesell, Gian-Marco Rignanese, Andrew Rosen, Matthias Scheffler, Jonathan SchmidtORCiD, Jimmy-Xuan Shen, Andrei Sobolev, Ravishankar Sundararaman, Cooper Tezak, Victor Trinquet, Joel Varley, Derek Vigil-Fowler, Duo Wang, David Waroquiers, Mingjian Wen, Han Yang, Hui Zheng, Jiongzhi Zheng, Zhuoying Zhu, Anubhav Jain |
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| Dokumenttyp: | Preprint |
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| Veröffentlichungsform: | Graue Literatur |
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| Sprache: | Englisch |
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| Titel des übergeordneten Werkes (Englisch): | ChemRxiv |
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| Jahr der Erstveröffentlichung: | 2025 |
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| Organisationseinheit der BAM: | 6 Materialchemie |
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| 6 Materialchemie / 6.0 Abteilungsleitung und andere |
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| Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
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| Verlag: | American Chemical Society (ACS) |
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| Verlagsort: | Washington, D.C. |
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| Erste Seite: | 1 |
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| Letzte Seite: | 66 |
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| DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten |
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| Freie Schlagwörter: | Automation; DFT; Digitalisation; Machine learned interatomic potentials; Machine learning; Materials design |
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| Themenfelder/Aktivitätsfelder der BAM: | Material |
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| Material / Materialdesign |
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| DOI: | 10.26434/chemrxiv-2025-tcr5h |
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| URN: | urn:nbn:de:kobv:b43-624487 |
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| ISSN: | 2573-2293 |
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| Zugehöriger Identifikator: | https://nbn-resolving.org/urn:nbn:de:kobv:b43-635759 |
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| Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
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| Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
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| Datum der Freischaltung: | 27.01.2025 |
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| Referierte Publikation: | Nein |
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| Schriftenreihen ohne Nummerierung: | Preprints der BAM |
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