Performance of Screened-Exchange Functionals for Band Gaps and Lattice Constants of Crystals

Cheng Zhang, Pragya Verma, Jiaxu Wang, Yiwei Liu, Xiao He, Ying Wang, Donald G. Truhlar, Zhonghua Liu

Research output: Contribution to journalArticlepeer-review

Abstract

Kohn-Sham density functional theory is the most widely used method for electronic structure calculations of solid-state systems. The screened-exchange functionals developed following the influential work of Scuseria and co-workers in 2003-2006 have significantly improved the accuracy of the predictions of solid-state properties. This work assesses six screened-exchange density functionals for the prediction of 60 band gaps (database BG60) and 68 lattice constants (database LC68). The band gaps are calculated with both consistently calculated lattice constants and experimental lattice constants. Results for the nonlocal screened-exchange functionals are compared with those for six widely used or recently developed local functionals. The results show that all the screened-exchange functionals have smaller mean absolute errors (MAEs) than any of the local functionals. All the functionals except HLE17 overestimate (on average) the lattice constants, and M06-SX gives the best performance among the compared functionals, with a MAE of 0.051 Å. All the functionals underestimate (on average) the band gaps, and M06-SX outperforms all other functionals, with a MAE of 0.47 eV. M06-SX also has the lowest root-mean-squared error for both LC68 and BG60. For the subdatabases of BG60, M06-SX shows better performance for ionic crystals and systems with large band gaps, while HSE12s gives better results for semiconductors and systems with small band gaps. Overall, M06-SX shows the best performance for solid-state systems, followed by N12-SX and HSE12s. The best-performing local functionals are M06-L, revM06-L, and HLE17 for band gaps and M06-L and revM06-L for lattice constants. We found that M06-SX, revM06-L, and N12-SX not only are well optimized for a broad array of chemical properties but also have very good performance for the databases in this paper, making them well-suited for applications involving heterogeneous chemistry.

Original languageEnglish (US)
Pages (from-to)311-323
Number of pages13
JournalJournal of Chemical Theory and Computation
Volume19
Issue number1
DOIs
StatePublished - Jan 10 2023

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China (Grants 21903024, 32071262, and 31770832), the Natural Science Foundation of Hunan Province (Grant 2020JJ5349), and the Science and Technology Innovation Program of Hunan Province (Grant 2020RC4023). X.H. acknowledges the financial support from the National Natural Science Foundation of China (Grants 21922301 and 22273023), the National Key R&D Program of China (Grant 2019YFA0905200), the Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, and the Fundamental Research Funds for the Central Universities. We thank the Bioinformatics Center of Hunan Normal University and the Supercomputer Center of East China Normal University (ECNU Multifunctional Platform for Innovation 001) for providing computer resources. The work at the University of Minnesota was supported in part by the National Science Foundation under Grant CHE-2054723.

Publisher Copyright:
© 2022 American Chemical Society.

PubMed: MeSH publication types

  • Journal Article

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