Statistically derived proxy potentials accelerate geometry optimization of crystal structures.

Antypov, Dmytro ORCID: 0000-0003-1893-7785, Collins, Christopher M ORCID: 0000-0002-0101-4426, Vasylenko, Andrij ORCID: 0000-0002-6933-0628, Gusev, Vladimir, Gaultois, Michael W ORCID: 0000-0003-2172-2507, Darling, George R ORCID: 0000-0001-9329-9993, Dyer, Matthew S ORCID: 0000-0002-4923-3003 and Rosseinsky, Matthew J ORCID: 0000-0002-1910-2483 (2024) Statistically derived proxy potentials accelerate geometry optimization of crystal structures. Chemphyschem : a European journal of chemical physics and physical chemistry. e202400254-e202400254.

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Official URL: http://dx.doi.org/10.1002/cphc.202400254

Abstract

The crystal structures of known materials contain the information about the interatomic interactions that produced these stable compounds. Similar to the use of reported protein structures to extract effective interactions between amino acids, that has been a useful tool in protein structure prediction, we demonstrate how to use this statistical paradigm to learn the effective inter-atomic interactions in crystalline inorganic solids. By analyzing the reported crystallographic data for inorganic materials, we have constructed statistically derived proxy potentials (SPPs) that can be used to assess how realistic or unusual a computer-generated structure is compared to the reported experimental structures. The SPPs can be directly used for structure optimization to improve this similarity metric, that we refer to as the SPP score. We apply such optimization step to markedly improve the quality of the input crystal structures for DFT calculations and demonstrate that the SPPs accelerate geometry optimization for three systems relevant to battery materials. As this approach is chemistry-agnostic and can be used at scale, we produced a database of all possible pair potentials in a tabulated form ready to use.

Item Type:	Article
Uncontrolled Keywords:	crystal structure, inorganic chemistry, interactions, optimization, statistical potential
Divisions:	Faculty of Science and Engineering > School of Physical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	17 Apr 2024 10:33
Last Modified:	26 Apr 2024 16:38
DOI:	10.1002/cphc.202400254
Open Access URL:	https://doi.org/10.1002/cphc.202400254
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3180409