arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2008.12412cond-mat.mtrl-sci
收藏

Predicting band gaps and band-edge positions of oxide perovskites using DFT and machine learning

Wei Li, Zigeng Wang, Xia Xiao, Zhiqiang Zhang, Anderson Janotti, Rajasekaran Sanguthevar, Bharat Medasani

英文摘要:

Density functional theory within the local or semilocal density approximations (DFT-LDA/GGA) has become a workhorse in 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. Accurate prediction of band gaps using firstprinciples methods is time consuming, requiring hybrid functionals, quasi-particle 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 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 ~1 eV comes from downward shifting the valence band and ~0.5 eV from uplifting the conduction band. The main chemical and structural factors determining the band gap correction are determined through a feature selection procedure.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑