Evaluation of the MACE force field architecture: From medicinal chemistry to materials science.

Autor:	Kovács DP; Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom., Batatia I; Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom.; ENS Paris-Saclay, Université Paris-Saclay, 91190 Gif-sur-Yvette, France., Arany ES; School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SP, United Kingdom., Csányi G; Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom.
Jazyk:	angličtina
Zdroj:	The Journal of chemical physics [J Chem Phys] 2023 Jul 28; Vol. 159 (4).
DOI:	10.1063/5.0155322
Abstrakt:	The MACE architecture represents the state of the art in the field of machine learning force fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we further evaluate MACE by fitting models for published benchmark datasets. We show that MACE generally outperforms alternatives for a wide range of systems, from amorphous carbon, universal materials modeling, and general small molecule organic chemistry to large molecules and liquid water. We demonstrate the capabilities of the model on tasks ranging from constrained geometry optimization to molecular dynamics simulations and find excellent performance across all tested domains. We show that MACE is very data efficient and can reproduce experimental molecular vibrational spectra when trained on as few as 50 randomly selected reference configurations. We further demonstrate that the strictly local atom-centered model is sufficient for such tasks even in the case of large molecules and weakly interacting molecular assemblies. (© 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).)
Databáze:	MEDLINE
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