Predicting band gaps and band-edge positions of oxide perovskites using density functional theory and machine learning
| Author(s) | Li, Wei | |
| Author(s) | Wang, Zigeng | |
| Author(s) | Xiao, Xia | |
| Author(s) | Zhang, Zhiqiang | |
| Author(s) | Janotti, Anderson | |
| Author(s) | Rajasekaran, Sanguthevar | |
| Author(s) | Medasani, Bharat | |
| Date Accessioned | 2023-01-05T15:10:20Z | |
| Date Available | 2023-01-05T15:10:20Z | |
| Publication Date | 2022-10-28 | |
| Description | This article was originally published in Physical Review B. The version of record is available at: https://doi.org/10.1103/PhysRevB.106.155156 | |
| Abstract | Density functional theory (DFT) within the local or semilocal density approximations, i.e., the local density approximation (LDA) or generalized gradient approximation (GGA), has become a workhorse in the electronic structure theory of solids, being extremely fast and reliable for energetics and structural properties, yet remaining highly inaccurate for predicting band gaps of semiconductors and insulators. The accurate prediction of band gaps using first-principles methods is time consuming, requiring hybrid functionals, quasiparticle GW, or quantum Monte Carlo methods. Efficiently correcting DFT-LDA/GGA band gaps and unveiling the main chemical and structural factors involved in this correction is desirable for discovering novel materials in high-throughput calculations. In this direction, we use DFT and machine learning techniques to correct band gaps and band-edge positions of a representative subset of ABO3 perovskite oxides. Relying on the results of HSE06 hybrid functional calculations as target values of band gaps, we find a systematic band-gap correction of ∼1.5 eV for this class of materials, where ∼1eV comes from downward shifting the valence band and ∼0.5eV from uplifting the conduction band. The main chemical and structural factors determining the band-gap correction are determined through a feature selection procedure. | |
| Sponsor | This work was supported by the National Science Foundation Grant No. EAGER-1843025. This research was also supported by the Xtreme Science and Engineering Discovery Environment (XSEDE) facility, National Science Foundation Grant No. ACI-1053575, and the Information Technologies (IT) resources at the University of Delaware, specifically the high-performance computing resources. This research was also supported in part by National Science Foundation Grants No. 1447711, No. 1743418, and No. 1843025. A.J. acknowledges support from NSF Faculty Early Career Development Program Grant No. DMR-1652994. W.L. was partly supported by the Laboratory Directed Research and Development Program of Los Alamos National Laboratory (LANL) under Project No. 20210087DR. LANL is operated by Triad National Security, LLC, for the National Nuclear Security Administration of the U.S. Department of Energy (Contract No. 89233218CNA000001). | |
| Citation | Li, Wei, Zigeng Wang, Xia Xiao, Zhiqiang Zhang, Anderson Janotti, Sanguthevar Rajasekaran, and Bharat Medasani. “Predicting Band Gaps and Band-Edge Positions of Oxide Perovskites Using Density Functional Theory and Machine Learning.” Phys. Rev. B 106, no. 15 (October 2022): 155156. https://doi.org/10.1103/PhysRevB.106.155156. | |
| ISSN | 1550-235X | |
| URL | https://udspace.udel.edu/handle/19716/31981 | |
| Language | en_US | |
| Publisher | Physical Review B | |
| Title | Predicting band gaps and band-edge positions of oxide perovskites using density functional theory and machine learning | |
| Type | Article |
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