Machine Learning in Materials Science
Open access
Peer reviewed
Abstract
Abstract
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.
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@article{Batatia2025foundation,
title = {A foundation model for atomistic materials chemistry},
author = {Ilyes Batatia and Philipp Benner and Yuan Chiang and A. M. Elena and Dávid Péter Kovács and Janosh Riebesell and Xavier R. Advincula and Mark Asta and Matthew Avaylon and William J. Baldwin and Fabian Berger and Noam Bernstein and Arghya Bhowmik and Filippo Bigi and Samuel M. Blau and Vlad Cărare and Michele Ceriotti and Sanggyu Chong and James P. Darby and Sandip De and Flaviano Della Pia and Volker L. Deringer and Rokas Elijošius and Zakariya El‐Machachi and Edvin Fako and Fabio Falcioni and Andrea C. Ferrari and John L. A. Gardner and Mikołaj J. Gawkowski and Annalena R. Genreith‐Schriever and Janine George and Rhys E. A. Goodall and Jonas Grandel and Clare P. Grey and Petr Grigorev and Shuang Han and Will Handley and Hendrik H. Heenen and Kersti Hermansson and Cheuk Hin Ho and S. Hofmann and Christian Holm and Jad Jaafar and Konstantin S. Jakob and Hyunwook Jung and Venkat Kapil and Aaron D. Kaplan and Nima Karimitari and James R. Kermode and Panagiotis Kourtis and Namu Kroupa and Jolla Kullgren and Matthew C. Kuner and Domantas Kuryla and Guoda Liepuoniute and Chen Lin and Johannes T. Margraf and Ioan-Bogdan Magdău and Angelos Michaelides and J. Harry Moore and Aakash Ashok Naik and Samuel P. Niblett and Sam Walton Norwood and Niamh O’Neill and Christoph Ortner and Kristin A. Persson and Karsten Reuter and Andrew Rosen and Louise A. M. Rosset and Lars L. Schaaf and Christoph Schran and Benjamin X. Shi and Eric Sivonxay and Tamás K. Stenczel and Christopher Sutton and Viktor Svahn and Thomas D. Swinburne and Jules Tilly and Cas van der Oord and Santiago Vargas and Eszter Varga-Umbrich and Tejs Vegge and Martin Vondrák and Yangshuai Wang and William C. Witt and Thomas Wolf and Fabian Zills and Gábor Cśanyi},
journal = {The Journal of Chemical Physics},
year = {2025},
doi = {10.1063/5.0297006},
url = {https://doi.org/10.1063/5.0297006}
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