Published December 6, 2022 | Version v2
Dataset Open

Accurate and efficient band-gap predictions for metal halide perovskites at finite temperature

  • 1. Physics Department, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland

* Contact person

Description

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.

Files

File preview

files_description.md

All files

Files (85.1 MiB)

Name Size
md5:db216e3dfbd7cf5009416d458622b9af
1.3 KiB Preview Download
md5:662c0277234e5b6e7259788b15960fdb
4.1 KiB Preview Download
md5:053d9131ea47741334fccd84e23bff4f
35.2 KiB Preview Download
md5:a191148bfd2e8793b37445537a437b7d
401.0 KiB Preview Download
md5:00bbca3156b324d3fa17643c691aa127
84.6 MiB Preview Download

References

Journal reference
H. Wang, A. Tal, T. Bischoff, P. Gono, A. Pasquarello, npj Comput. Mater. 8, 237 (2022)., doi: 10.1038/s41524-022-00869-6