A foundation model for atomistic materials chemistry
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| author | Batatia, Ilyes Benner, Philipp Chiang, Yuan Elena, Alin M. Kovács, Dávid P. Riebesell, Janosh Advincula, Xavier R. Asta, Mark Avaylon, Matthew Baldwin, William J. Berger, Fabian Bernstein, Noam Bhowmik, Arghya Bigi, Filippo Blau, Samuel M. Cărare, Vlad Ceriotti, Michele Chong, Sanggyu Darby, James P. De, Sandip Della Pia, Flaviano Deringer, Volker L. Elijošius, Rokas El-Machachi, Zakariya Falcioni, Fabio Fako, Edvin Ferrari, Andrea C. Gardner, John L. A. Gawkowski, Mikolaj J. Genreith-Schriever, Annalena George, Janine Goodall, Rhys E. A. Grandel, Jonas Grey, Clare P. Grigorev, Petr Han, Shuang Handley, Will Heenen, Hendrik H. Hermansson, Kersti Holm, Christian Ho, Cheuk Hin Hofmann, Stephan Jaafar, Jad Jakob, Konstantin S. Jung, Hyunwook Kapil, Venkat Kaplan, Aaron D. Karimitari, Nima Kermode, James R. Kourtis, Panagiotis Kroupa, Namu Kullgren, Jolla Kuner, Matthew C. Kuryla, Domantas Liepuoniute, Guoda Lin, Chen Margraf, Johannes T. Magdău, Ioan-Bogdan Michaelides, Angelos Moore, J. Harry Naik, Aakash A. Niblett, Samuel P. Norwood, Sam Walton O'Neill, Niamh Ortner, Christoph Persson, Kristin A. Reuter, Karsten Rosen, Andrew S. 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 |
| author_facet | Batatia, Ilyes Benner, Philipp Chiang, Yuan Elena, Alin M. Kovács, Dávid P. Riebesell, Janosh Advincula, Xavier R. Asta, Mark Avaylon, Matthew Baldwin, William J. Berger, Fabian Bernstein, Noam Bhowmik, Arghya Bigi, Filippo Blau, Samuel M. Cărare, Vlad Ceriotti, Michele Chong, Sanggyu Darby, James P. De, Sandip Della Pia, Flaviano Deringer, Volker L. Elijošius, Rokas El-Machachi, Zakariya Falcioni, Fabio Fako, Edvin Ferrari, Andrea C. Gardner, John L. A. Gawkowski, Mikolaj J. Genreith-Schriever, Annalena George, Janine Goodall, Rhys E. A. Grandel, Jonas Grey, Clare P. Grigorev, Petr Han, Shuang Handley, Will Heenen, Hendrik H. Hermansson, Kersti Holm, Christian Ho, Cheuk Hin Hofmann, Stephan Jaafar, Jad Jakob, Konstantin S. Jung, Hyunwook Kapil, Venkat Kaplan, Aaron D. Karimitari, Nima Kermode, James R. Kourtis, Panagiotis Kroupa, Namu Kullgren, Jolla Kuner, Matthew C. Kuryla, Domantas Liepuoniute, Guoda Lin, Chen Margraf, Johannes T. Magdău, Ioan-Bogdan Michaelides, Angelos Moore, J. Harry Naik, Aakash A. Niblett, Samuel P. Norwood, Sam Walton O'Neill, Niamh Ortner, Christoph Persson, Kristin A. Reuter, Karsten Rosen, Andrew S. 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_00096 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A foundation model for atomistic materials chemistry Batatia, Ilyes Benner, Philipp Chiang, Yuan Elena, Alin M. Kovács, Dávid P. Riebesell, Janosh Advincula, Xavier R. Asta, Mark Avaylon, Matthew Baldwin, William J. Berger, Fabian Bernstein, Noam Bhowmik, Arghya Bigi, Filippo Blau, Samuel M. Cărare, Vlad Ceriotti, Michele Chong, Sanggyu Darby, James P. De, Sandip Della Pia, Flaviano Deringer, Volker L. Elijošius, Rokas El-Machachi, Zakariya Falcioni, Fabio Fako, Edvin Ferrari, Andrea C. Gardner, John L. A. Gawkowski, Mikolaj J. Genreith-Schriever, Annalena George, Janine Goodall, Rhys E. A. Grandel, Jonas Grey, Clare P. Grigorev, Petr Han, Shuang Handley, Will Heenen, Hendrik H. Hermansson, Kersti Holm, Christian Ho, Cheuk Hin Hofmann, Stephan Jaafar, Jad Jakob, Konstantin S. Jung, Hyunwook Kapil, Venkat Kaplan, Aaron D. Karimitari, Nima Kermode, James R. Kourtis, Panagiotis Kroupa, Namu Kullgren, Jolla Kuner, Matthew C. Kuryla, Domantas Liepuoniute, Guoda Lin, Chen Margraf, Johannes T. Magdău, Ioan-Bogdan Michaelides, Angelos Moore, J. Harry Naik, Aakash A. Niblett, Samuel P. Norwood, Sam Walton O'Neill, Niamh Ortner, Christoph Persson, Kristin A. Reuter, Karsten Rosen, Andrew S. 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 Chemical Physics Materials Science 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. |
| title | A foundation model for atomistic materials chemistry |
| topic | Chemical Physics Materials Science |
| url | https://arxiv.org/abs/2401.00096 |