Predicting the volumes of crystals

Chu, Iek-Heng; Roychowdhury, Sayan; Han, Daehui; Jain, Anubhav; Ong, Shyue Ping

doi:10.1016/j.commatsci.2018.01.040

Title: Predicting the volumes of crystals

Journal Article · Tue Feb 20 00:00:00 EST 2018 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2018.01.040· OSTI ID:1462707

Chu, Iek-Heng ^[1]; Roychowdhury, Sayan ^[2]; Han, Daehui ^[1]; Jain, Anubhav ^[2];

^[1]

University of California San Diego, La Jolla, CA (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

New crystal structures are frequently derived by performing ionic substitutions on known crystal structures. These derived structures are then used in further experimental analysis, or as the initial guess for structural optimization in electronic structure calculations, both of which usually require a reasonable guess of the lattice parameters. In this work, we propose two lattice prediction schemes to improve the initial guess of a candidate crystal structure. The first scheme relies on a one-to-one mapping of species in the candidate crystal structure to a known crystal structure, while the second scheme relies on data-mined minimum atom pair distances to predict the crystal volume of the candidate crystal structure and does not require a reference structure. We demonstrate that the two schemes can effectively predict the volumes within mean absolute errors (MAE) as low as 3.8% and 8.2%. Here we also discuss the various factors that may impact the performance of the schemes. Implementations for both schemes are available in the open-source pymatgen software.

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Research Organization:: Lawrence Berkeley National Laboratory, Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE); USDOE Office of Science (SC), Office of Workforce Development for Teachers & Scientists (WDTS); National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-05CH11231; ACI-1053575; AC02-05-CH11231; KC23MP

OSTI ID:: 1462707

Alternate ID(s):: OSTI ID: 1693872

Journal Information:: Computational Materials Science, Vol. 146, Issue C; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 2 works

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