ACEpotentials.jl: A Julia implementation of the atomic cluster expansion.

Autor:	Witt WC; Department of Materials Science and Metallurgy, University of Cambridge, Cambridge, United Kingdom., van der Oord C; Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom., Gelžinytė E; Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom., Järvinen T; Department of Mathematics, University of British Columbia, 1984 Mathematics Road, Vancouver, British Columbia V6T 1Z2, Canada., Ross A; Department of Mathematics, University of British Columbia, 1984 Mathematics Road, Vancouver, British Columbia V6T 1Z2, Canada., Darby JP; Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom., Ho CH; Department of Mathematics, University of British Columbia, 1984 Mathematics Road, Vancouver, British Columbia V6T 1Z2, Canada., Baldwin WJ; Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom., Sachs M; School of Mathematics, University of Birmingham, Birmingham B15 2TT, United Kingdom., Kermode J; Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom., Bernstein N; Center for Materials Physics and Technology, U.S. Naval Research Laboratory, Washington, District of Columbia 20375, USA., Csányi G; Engineering Laboratory, University of Cambridge, Cambridge CB2 1PZ, United Kingdom., Ortner C; Department of Mathematics, University of British Columbia, 1984 Mathematics Road, Vancouver, British Columbia V6T 1Z2, Canada.
Jazyk:	angličtina
Zdroj:	The Journal of chemical physics [J Chem Phys] 2023 Oct 28; Vol. 159 (16).
DOI:	10.1063/5.0158783
Abstrakt:	We introduce ACEpotentials.jl, a Julia-language software package that constructs interatomic potentials from quantum mechanical reference data using the Atomic Cluster Expansion [R. Drautz, Phys. Rev. B 99, 014104 (2019)]. As the latter provides a complete description of atomic environments, including invariance to overall translation and rotation as well as permutation of like atoms, the resulting potentials are systematically improvable and data efficient. Furthermore, the descriptor's expressiveness enables use of a linear model, facilitating rapid evaluation and straightforward application of Bayesian techniques for active learning. We summarize the capabilities of ACEpotentials.jl and demonstrate its strengths (simplicity, interpretability, robustness, performance) on a selection of prototypical atomistic modelling workflows. (© 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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