تقرير
A foundation model for atomistic materials chemistry
| العنوان: | A foundation model for atomistic materials chemistry |
|---|---|
| المؤلفون: | 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, Blau, Samuel M., Cărare, Vlad, Darby, James P., De, Sandip, Della Pia, Flaviano, Deringer, Volker L., Elijošius, Rokas, El-Machachi, Zakariya, Falcioni, Fabio, Fako, Edvin, Ferrari, Andrea C., Genreith-Schriever, Annalena, George, Janine, Goodall, Rhys E. A., Grey, Clare P., Grigorev, Petr, Han, Shuang, Handley, Will, Heenen, Hendrik H., Hermansson, Kersti, Holm, Christian, Jaafar, Jad, Hofmann, Stephan, Jakob, Konstantin S., Jung, Hyunwook, Kapil, Venkat, Kaplan, Aaron D., Karimitari, Nima, Kermode, James R., Kroupa, Namu, Kullgren, Jolla, Kuner, Matthew C., Kuryla, Domantas, Liepuoniute, Guoda, 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., 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, Varga-Umbrich, Eszter, Vegge, Tejs, Vondrák, Martin, Wang, Yangshuai, Witt, William C., Zills, Fabian, Csányi, Gábor |
| سنة النشر: | 2023 |
| المجموعة: | Condensed Matter Physics (Other) |
| مصطلحات موضوعية: | Physics - Chemical Physics, Condensed Matter - Materials Science |
| الوصف: | Machine-learned force fields have transformed the atomistic modelling of materials by enabling simulations of ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) the significant computational and human effort that must go into development and validation of potentials for each particular system of interest; and (ii) a general lack of transferability from one chemical system to the next. Here, using the state-of-the-art MACE architecture we introduce a single general-purpose ML model, trained on a public database of 150k inorganic crystals, that is capable of running stable molecular dynamics on 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 problems in the physical sciences, including the 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 and as a starting or "foundation model" for any atomistic system of interest and is thus a step towards democratising the revolution of ML force fields by lowering the barriers to entry. Comment: 119 pages, 63 figures, 37MB PDF |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2401.00096Test |
| رقم الانضمام: | edsarx.2401.00096 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |