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Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab\u00a0initio 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\u2014and its qualitative and at times quantitative accuracy\u2014on 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 \u201cfoundation\u201d 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\u00a0initio 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. 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H.\n            Ho\n          , C.Ortner, and Y.Wang, \u201cFlexible uncertainty calibration for machine-learned interatomic potentials,\u201d arXiv:2510.00721 (2025)."},{"key":"2025111306434710400_c332","volume-title":"Pattern Recognition and Machine Learning","year":"2006"}],"container-title":["The Journal of Chemical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.aip.org\/aip\/jcp\/article-pdf\/doi\/10.1063\/5.0297006\/20801246\/184110_1_5.0297006.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.aip.org\/aip\/jcp\/article-pdf\/doi\/10.1063\/5.0297006\/20801246\/184110_1_5.0297006.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T11:44:05Z","timestamp":1763034245000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.aip.org\/jcp\/article\/163\/18\/184110\/3372267\/A-foundation-model-for-atomistic-materials"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,13]]},"references-count":332,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2025,11,14]]}},"URL":"https:\/\/doi.org\/10.1063\/5.0297006","relation":{},"ISSN":["0021-9606","1089-7690"],"issn-type":[{"value":"0021-9606","type":"print"},{"value":"1089-7690","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,11,14]]},"published":{"date-parts":[[2025,11,13]]},"article-number":"184110"}}