Alex M. Ganose, Hrushikesh Sahasrabuddhe, Mark Asta, Kevin Beck, Tathagata Biswas, Alexander Bonkowski, Joana Bustamante, Xin Chen, Yuan Chiang, Daryl C. Chrzan, Jacob Clary, Orion A. Cohen, Christina Ertural, Max C. Gallant, Janine George, Sophie Gerits, Rhys E. A. Goodall, Rishabh D. Guha, Geoffroy Hautier, Matthew Horton, T. J. Inizan, Aaron D. Kaplan, Ryan S. Kingsbury, Matthew C. Kuner, Bryant Li, Xavier Linn, Matthew J. McDermott, Rohith Srinivaas Mohanakrishnan, Aakash A. Naik, Jeffrey B. Neaton, Shehan M. Parmar, Kristin A. Persson, Guido Petretto, Thomas A. R. Purcell, Francesco Ricci, Benjamin Rich, Janosh Riebesell, Gian-Marco Rignanese, Andrew S. Rosen, Matthias Scheffler, Jonathan Schmidt, Jimmy-Xuan Shen, Andrei Sobolev, Ravishankar Sundararaman, Cooper Tezak, Victor Trinquet, Joel B. 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 M. GanoseORCiD, Hrushikesh SahasrabuddheORCiD, Mark Asta, Kevin Beck, Tathagata BiswasORCiD, Alexander Bonkowski, Joana BustamanteORCiD, Xin Chen, Yuan Chiang, Daryl C. Chrzan, Jacob Clary, Orion A. Cohen, Christina ErturalORCiD, Max C. Gallant, Janine GeorgeORCiD, Sophie Gerits, Rhys E. A. Goodall, Rishabh D. GuhaORCiD, Geoffroy Hautier, Matthew Horton, T. J. Inizan, Aaron D. KaplanORCiD, Ryan S. KingsburyORCiD, Matthew C. Kuner, Bryant LiORCiD, Xavier Linn, Matthew J. McDermottORCiD, Rohith Srinivaas Mohanakrishnan, Aakash A. NaikORCiD, Jeffrey B. Neaton, Shehan M. ParmarORCiD, Kristin A. PerssonORCiD, Guido Petretto, Thomas A. R. PurcellORCiD, Francesco RicciORCiD, Benjamin RichORCiD, Janosh RiebesellORCiD, Gian-Marco RignaneseORCiD, Andrew S. RosenORCiD, Matthias Scheffler, Jonathan SchmidtORCiD, Jimmy-Xuan Shen, Andrei Sobolev, Ravishankar SundararamanORCiD, Cooper Tezak, Victor TrinquetORCiD, Joel B. 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: | Zeitschriftenartikel |
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| Veröffentlichungsform: | Verlagsliteratur |
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| Sprache: | Englisch |
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| Titel des übergeordneten Werkes (Englisch): | Digital discovery |
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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: | Royal Society of Chemistry (RSC) |
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| Verlagsort: | Cambridge |
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| Aufsatznummer: | d5dd00019j |
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| Erste Seite: | 1 |
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| Letzte Seite: | 30 |
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| DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften |
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| Freie Schlagwörter: | Automation; Bonding analysis; DFT workflows; Materials design; Phonons; Thermal conductivity |
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| Themenfelder/Aktivitätsfelder der BAM: | Material |
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| Material / Materialdesign |
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| DOI: | 10.1039/d5dd00019j |
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| URN: | urn:nbn:de:kobv:b43-635759 |
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| ISSN: | 2635-098X |
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| Zugehöriger Identifikator: | https://nbn-resolving.org/urn:nbn:de:kobv:b43-624487 |
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| Zugehöriger Identifikator: | https://nbn-resolving.org/urn:nbn:de:kobv:b43-640297 |
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| Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
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| Lizenz (Deutsch): | Creative Commons - Namensnennung 3.0 |
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| Datum der Freischaltung: | 02.07.2025 |
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| Referierte Publikation: | Ja |
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| Datum der Eintragung als referierte Publikation: | 16.07.2025 |
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| Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |
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