期刊
PHYSICAL REVIEW B
卷 93, 期 11, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevB.93.115104
关键词
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资金
- Japan Society for the Promotion of Science (JSPS) [25106005]
- Materials research by Information Integration Initiative (MI2I) from Japan Science and Technology Agency
- JSPS [2604376]
- Grants-in-Aid for Scientific Research [14F04376] Funding Source: KAKEN
Machine learning techniques are applied to make prediction models of the G(0)W(0) band gaps for 270 inorganic compounds using Kohn-Sham (KS) band gaps, cohesive energy, crystalline volume per atom, and other fundamental information of constituent elements as predictors. Ordinary least squares regression (OLSR), least absolute shrinkage and selection operator, and nonlinear support vector regression (SVR) methods are applied with two levels of predictor sets. When the KS band gap by generalized gradient approximation of Perdew-Burke-Ernzerhof (PBE) or modified Becke-Johnson (mBJ) is used as a single predictor, the OLSR model predicts the G(0)W(0) band gap of randomly selected test data with the root-mean-square error (RMSE) of 0.59 eV. When KS band gap by PBE and mBJ methods are used together with a set of predictors representing constituent elements and compounds, the RMSE decreases significantly. The best model by SVR yields the RMSE of 0.24 eV. Band gaps estimated in this way should be useful as predictors for virtual screening of a large set of materials.
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