Accurate and efficient band-gap predictions for metal halide perovskites at finite temperature: corresponding atomic structures at the certain temperature

DOI

We develop a computationally efficient scheme to accurately determine finite-temperature band gaps. We here focus on materials belonging to the class ABX3 (A = Rb, Cs; B = Ge, Sn, Pb; and X = F, Cl, Br, I), which includes halide perovskites. First, an initial estimate of the band gap is provided for the ideal crystalline structure through the use of a range-separated hybrid functional, in which the parameters are determined nonempirically from the electron density and the high-frequency dielectric constant. Next, we consider two kinds of band-gap corrections to account for spin-orbit coupling and thermal vibrations including zero-point motions. In particular, the latter effect is accounted for through the special displacement method, which consists in using a single distorted configuration obtained from the vibrational frequencies and eigenmodes, thereby avoiding lengthy molecular dynamics. The sequential consideration of both corrections systematically improves the band gaps, reaching a mean absolute error of 0.17 eV with respect to experimental values. The computational efficiency of our scheme stems from the fact that only a single calculation at the hybrid-functional level is required and that it is sufficient to evaluate the corrections at the semilocal level of theory. Our scheme is particularly convenient for large-size systems and for the screening of large databases of materials. This entry provides the ideal atomic structures and the distorted atomic structures at certain temperature including zero-point motions, generated by special displacement method.

Identifier
DOI https://doi.org/10.24435/materialscloud:b2-bj
Related Identifier https://doi.org/10.48550/arXiv.2203.01002
Related Identifier https://archive.materialscloud.org/communities/mcarchive
Related Identifier https://doi.org/10.24435/materialscloud:f7-64
Metadata Access https://archive.materialscloud.org/oai2d?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:1279
Provenance
Creator Wang, Haiyuan; Tal, Alexey; Bischoff, Thomas; Gono, Patrick; Pasquarello, Alfredo
Publisher Materials Cloud
Contributor Wang, Haiyuan
Publication Year 2022
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International; https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type info:eu-repo/semantics/other
Format application/zip; text/markdown
Discipline Materials Science and Engineering