Ilyes Batatia, Philipp Benner, Yuan Chiang, Alin M. Elena, Dávid P. Kovács, Janosh Riebesell, Xavier R. Advincula, Mark Asta, Matthew Avaylon, William J. Baldwin, Fabian Berger, Noam Bernstein, Arghya Bhowmik, Filippo Bigi, Samuel M. Blau, Vlad Cărare, Michele Ceriotti, Sanggyu Chong, James P. Darby, Sandip De, Flaviano Della Pia, Volker L. Deringer, Rokas Elijošius, Zakariya El-Machachi, Edvin Fako, Fabio Falcioni, Andrea C. Ferrari, John L. A. Gardner, Mikołaj J. Gawkowski, Annalena Genreith-Schriever, Janine George, Rhys E. A. Goodall, Jonas Grandel, Clare P. Grey, Petr Grigorev, Shuang Han, Will Handley, Hendrik H. Heenen, Kersti Hermansson, Cheuk Hin Ho, Stephan Hofmann, Christian Holm, Jad Jaafar, Konstantin S. Jakob, Hyunwook Jung, Venkat Kapil, Aaron D. Kaplan, Nima Karimitari, Aakash A. Naik, Gábor Csányi
- Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model—and itsAtomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model—and its qualitative and at times quantitative accuracy—on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or “foundation” model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.…


Metadaten| Autor*innen: | Ilyes BatatiaORCiD, Philipp BennerORCiD, Yuan ChiangORCiD, Alin M. ElenaORCiD, Dávid P. KovácsORCiD, Janosh RiebesellORCiD, Xavier R. AdvinculaORCiD, Mark AstaORCiD, Matthew Avaylon, William J. BaldwinORCiD, Fabian BergerORCiD, Noam BernsteinORCiD, Arghya BhowmikORCiD, Filippo BigiORCiD, Samuel M. BlauORCiD, Vlad CărareORCiD, Michele CeriottiORCiD, Sanggyu ChongORCiD, James P. DarbyORCiD, Sandip DeORCiD, Flaviano Della PiaORCiD, Volker L. DeringerORCiD, Rokas ElijošiusORCiD, Zakariya El-MachachiORCiD, Edvin FakoORCiD, Fabio FalcioniORCiD, Andrea C. FerrariORCiD, John L. A. GardnerORCiD, Mikołaj J. GawkowskiORCiD, Annalena Genreith-SchrieverORCiD, Janine GeorgeORCiD, Rhys E. A. GoodallORCiD, Jonas GrandelORCiD, Clare P. GreyORCiD, Petr GrigorevORCiD, Shuang HanORCiD, Will HandleyORCiD, Hendrik H. HeenenORCiD, Kersti HermanssonORCiD, Cheuk Hin HoORCiD, Stephan HofmannORCiD, Christian HolmORCiD, Jad JaafarORCiD, Konstantin S. JakobORCiD, Hyunwook JungORCiD, Venkat KapilORCiD, Aaron D. KaplanORCiD, Nima KarimitariORCiD, Aakash A. Naik, Gábor CsányiORCiD |
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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): | The Journal of Chemical Physics |
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| Jahr der Erstveröffentlichung: | 2025 |
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| Organisationseinheit der BAM: | 6 Materialchemie |
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| 6 Materialchemie / 6.6 Digitale Materialchemie |
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| VP Vizepräsident |
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| VP Vizepräsident / VP.1 eScience |
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| Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
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| Verlag: | AIP Publishing |
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| Jahrgang/Band: | 163 |
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| Ausgabe/Heft: | 18 |
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| Aufsatznummer: | 184110 |
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| Erste Seite: | 1 |
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| Letzte Seite: | 89 |
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| DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten |
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| Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Angewandte Physik |
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| Freie Schlagwörter: | Batteries; Materials Design; Nanoparticles; Thermal Conducitivity |
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| Themenfelder/Aktivitätsfelder der BAM: | Energie |
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| Energie / Elektrische Energiespeicher und -umwandlung |
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| Material |
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| Material / Advanced Materials |
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| Material / Materialdesign |
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| DOI: | 10.1063/5.0297006 |
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| URN: | urn:nbn:de:kobv:b43-647829 |
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| ISSN: | 0021-9606 |
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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: | 20.11.2025 |
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| Referierte Publikation: | Ja |
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| Datum der Eintragung als referierte Publikation: | 01.12.2025 |
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| Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |
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