Authors: Ilyes Batatia | Philipp Benner | Yuan Chiang | A. M. Elena | Dávid Péter 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 R. 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 | James R. Kermode | Panagiotis Kourtis | Namu Kroupa | Jolla Kullgren | Matthew C. Kuner | Domantas Kuryla | Guoda Liepuoniute | Chen Lin | 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 | Louise A. M. Rosset | Lars L. Schaaf | Christoph Schran | Benjamin X. Shi | Eric Sivonxay | Tamás K. Stenczel | Christopher Sutton | Viktor Svahn | Thomas D. Swinburne | Jules Tilly | Cas van der Oord | Santiago Vargas | Eszter Varga-Umbrich | Tejs Vegge | Martin Vondrák | Yangshuai Wang | William C. Witt | Thomas Wolf | Fabian Zills | Gábor Cśanyi
Source: The Journal of Chemical Physics (article, 2025-11-13)
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 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. Leverage (statistics) ; Foundation (evidence) ; Ab initio ; Transferability ; Molecular dynamics ; Underpinning ; Force field (fiction) ; Set (abstract data type) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum many-body systems