論文

査読有り
2016年3月

Prediction model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques

PHYSICAL REVIEW B
  • Joohwi Lee
  • ,
  • Atsuto Seko
  • ,
  • Kazuki Shitara
  • ,
  • Keita Nakayama
  • ,
  • Isao Tanaka

93
11
開始ページ
115104
終了ページ
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1103/PhysRevB.93.115104
出版者・発行元
AMER PHYSICAL SOC

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.

Web of Science ® 被引用回数 : 153

リンク情報
DOI
https://doi.org/10.1103/PhysRevB.93.115104
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000371402800004&DestApp=WOS_CPL
ID情報
  • DOI : 10.1103/PhysRevB.93.115104
  • ISSN : 2469-9950
  • eISSN : 2469-9969
  • Web of Science ID : WOS:000371402800004

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