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Title: Predicting the volumes of crystals

Abstract

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

Authors:
 [1];  [2];  [1];  [2]; ORCiD logo [1]
  1. University of California San Diego, La Jolla, CA (United States). Department of NanoEngineering
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Energy Technologies Area
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory, Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC).
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division; USDOE Office of Science (SC), Workforce Development for Teachers and Scientists (WDTS) (SC-27); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1462707
DOE Contract Number:  
AC02-05-CH11231; AC02-05CH11231
Resource Type:
Journal Article
Journal Name:
Computational Materials Science
Additional Journal Information:
Journal Volume: 146; Journal Issue: C; Journal ID: ISSN 0927-0256
Publisher:
Elsevier
Country of Publication:
United States
Language:
English

Citation Formats

Chu, Iek-Heng, Roychowdhury, Sayan, Han, Daehui, Jain, Anubhav, and Ong, Shyue Ping. Predicting the volumes of crystals. United States: N. p., 2018. Web. doi:10.1016/j.commatsci.2018.01.040.
Chu, Iek-Heng, Roychowdhury, Sayan, Han, Daehui, Jain, Anubhav, & Ong, Shyue Ping. Predicting the volumes of crystals. United States. doi:10.1016/j.commatsci.2018.01.040.
Chu, Iek-Heng, Roychowdhury, Sayan, Han, Daehui, Jain, Anubhav, and Ong, Shyue Ping. Sun . "Predicting the volumes of crystals". United States. doi:10.1016/j.commatsci.2018.01.040.
@article{osti_1462707,
title = {Predicting the volumes of crystals},
author = {Chu, Iek-Heng and Roychowdhury, Sayan and Han, Daehui and Jain, Anubhav and Ong, Shyue Ping},
abstractNote = {New crystal structures are frequently derived by performing ionic substitutions on known crystal struc-tures. These derived structures are then used in further experimental analysis, or as the initial guess forstructural optimization in electronic structure calculations, both of which usually require a reasonableguess of the lattice parameters. In this work, we propose two lattice prediction schemes to improvethe initial guess of a candidate crystal structure. The first scheme relies on a one-to-one mapping of spe-cies in the candidate crystal structure to a known crystal structure, while the second scheme relies ondata-mined minimum atom pair distances to predict the crystal volume of the candidate crystal structureand does not require a reference structure. We demonstrate that the two schemes can effectively predictthe volumes within mean absolute errors (MAE) as low as 3.8% and 8.2%. We also discuss the various fac-tors that may impact the performance of the schemes. Implementations for both schemes are available inthe open-source pymatgen software.},
doi = {10.1016/j.commatsci.2018.01.040},
journal = {Computational Materials Science},
issn = {0927-0256},
number = C,
volume = 146,
place = {United States},
year = {2018},
month = {4}
}