Authors: Ilyes Batatia | Philipp Benner | Yuan Chiang | Alin M. Elena | Dávid Péter Kovács | Janosh Riebesell | Xavier R. Advincula | Mark Asta | Avaylon, Matthew | William J. Baldwin | Berger, Fabian | Noam Bernstein | Arghya Bhowmik | Bigi, Filippo | Samuel M. Blau | Vlad Cărare | Ceriotti, Michele | Chong, Sanggyu | James P. Darby | Sandip De | Flaviano Della Pia | Volker L. Deringer | Rokas Elijošius | Zakariya El‐Machachi | Falcioni, Fabio | Edvin Fako | Andrea C. Ferrari | Gardner, John L. A. | Gawkowski, Mikolaj J. | Annalena R. Genreith‐Schriever | Janine George | Rhys E. A. Goodall | Grandel, Jonas | Clare P. Grey | Grigorev, Petr | Shuang Han | Will Handley | Hendrik H. Heenen | Kersti Hermansson | Christian Holm | Ho, Cheuk Hin | Stephan Hofmann | Jad Jaafar | Konstantin S. Jakob | Hyunwook Jung | Venkat Kapil | Aaron D. Kaplan | Nima Karimitari | Kermode, James R. | Kourtis, Panagiotis | Namu Kroupa | Jolla Kullgren | Matthew C. Kuner | Domantas Kuryla | Guoda Liepuoniute | Lin, Chen | Johannes T. Margraf | Ioan-Bogdan Magdău | Angelos Michaelides | J. Harry Moore | Aakash Ashok Naik | Samuel P. Niblett | Sam Walton Norwood | Niamh O’Neill | Christoph Ortner | Kristin A. Persson | Karsten Reuter | Andrew Rosen | Rosset, Louise A. M. | Lars L. Schaaf | Christoph Schran | Shi, Benjamin X. | Eric Sivonxay | Tamás K. Stenczel | Viktor Svahn | Christopher Sutton | Swinburne, Thomas D. | Tilly, Jules | van der Oord | Vargas, Santiago | Eszter Varga-Umbrich | Tejs Vegge | Martin Vondrák | Yangshuai Wang | William C. Witt | Wolf, Thomas | Fabian Zills | Gábor Csányi
Source: arXiv (Cornell University) (preprint, 2023-12-29)
Field: Materials Science — Materials Chemistry

A foundation model for atomistic materials chemistry 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 ML force fields have largely been limited by: (i) the substantial computational and human effort of developing and validating 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 get reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step towards democratising the revolution in atomic-scale modeling that has been brought about by ML force fields. Transferability ; Foundation (evidence) ; Computer science ; Ab initio ; Molecular dynamics ; Statistical physics ; Nanotechnology ; Computational science ; Chemistry ; Computational chemistry ; Materials science ; Physics ; Machine learning Machine Learning in Materials Science ; Scientific Computing and Data Management