A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang, Alin M. Elena, Dávid Péter Kovács, Janosh Riebesell, Xavier R. Advincula, Mark Asta, William J. Baldwin, Noam Bernstein, Arghya Bhowmik, Samuel M. Blau, Vlad Cărare, James P. Darby, Sandip De, Flaviano Della Pia, Volker L. Deringer, Rokas Elijošius, Zakariya El‐Machachi, Edvin Fako, Andrea C. Ferrari, Annalena R. Genreith‐Schriever, Janine George, Rhys E. A. Goodall, Clare P. Grey, Shuang Han, Will Handley, Hendrik H. Heenen, Kersti Hermansson, Christian Holm, Jad Jaafar, Stephan Hofmann, Konstantin S. Jakob, Hyunwook Jung, Venkat Kapil, Aaron D. Kaplan, Nima Karimitari, Namu Kroupa, Jolla Kullgren, Matthew C. Kuner, Domantas Kuryla, Guoda Liepuoniute, 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, Lars L. Schaaf, Christoph Schran, Eric Sivonxay, Tamás K. Stenczel, Viktor Svahn, Christopher Sutton, van der Oord, Eszter Varga-Umbrich, Tejs Vegge, Martin Vondrák, Yangshuai Wang, William C. Witt, Fabian Zills, Gábor Csányi, Rosset, Louise A. M., Schaaf, Lars L., Schran, Christoph, Shi, Benjamin X., Sivonxay, Eric, Stenczel, Tamás K., Svahn, Viktor, Sutton, Christopher, Swinburne, Thomas D., Tilly, Jules, van der Oord, Cas, Vargas, Santiago, Varga-Umbrich, Eszter, Vegge, Tejs, Vondrák, Martin, Wang, Yangshuai, Witt, William C., Wolf, Thomas, Zills, Fabian, Csányi, Gábor
Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
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. H ...
owever, 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.
Results & Benchmarks
Some benchmark signal exists in the extracted evidence, but it is not structured strongly enough yet for a confident benchmark decision.
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.
Implementation Evidence Summary
ai-boost/awesome-ai-for-science is the closest maintained adjacent implementation (Title overlap with paper keywords (50%)). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 1660 GitHub stars.
Reproduction Risks
- Adjacent implementations are not paper-verified
- Recommended repository is adjacent and not paper-verified.
Hardware Notes
Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Evidence disclosure
Evidence graph: 3 refs, 3 links.
Utility signals: depth 100/100, grounding 85/100, status high.
Implementation Status
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Reproduction readiness
Hardware requirements
- Expect multi-day setup/compute for meaningful reproduction based on current guidance.
No verified implementation available
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Closest related implementations
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- ai-boost/awesome-ai-for-scienceAdjacentConfidence: MediumStars: 1,660
Title overlap with paper keywords (50%)
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Research context
243
Citations
0
References
Tasks
Transferability, Foundation (evidence), Computer science, Ab initio, Molecular dynamics, Nanotechnology, Computational science, Materials science
Methods
None detected
Domains
Statistical physics, Chemistry, Computational chemistry, Materials Chemistry
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