論文

査読有り
2014年2月18日

Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids

Physical Review B - Condensed Matter and Materials Physics
  • Atsuto Seko
  • ,
  • Tomoya Maekawa
  • ,
  • Koji Tsuda
  • ,
  • Isao Tanaka

89
5
開始ページ
054303
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.89.054303
出版者・発行元
AMER PHYSICAL SOC

A combination of systematic density-functional theory (DFT) calculations and machine learning techniques has a wide range of potential applications. This study presents an application of the combination of systematic DFT calculations and regression techniques to the prediction of the melting temperature for single and binary compounds. Here we adopt the ordinary least-squares regression, partial least-squares regression, support vector regression, and Gaussian process regression. Among the four kinds of regression techniques, SVR provides the best prediction. The inclusion of physical properties computed by the DFT calculation to a set of predictor variables makes the prediction better. In addition, limitation of the predictive power is shown when extrapolation from the training dataset is required. Finally, a simulation to find the highest melting temperature toward the efficient materials design using kriging is demonstrated. The kriging design finds the compound with the highest melting temperature much faster than random designs. This result may stimulate the application of kriging to efficient materials design for a broad range of applications. © 2014 American Physical Society.

リンク情報
DOI
https://doi.org/10.1103/PhysRevB.89.054303
URL
http://repository.kulib.kyoto-u.ac.jp/dspace/handle/2433/187034
ID情報
  • DOI : 10.1103/PhysRevB.89.054303
  • ISSN : 1098-0121
  • ISSN : 1550-235X
  • SCOPUS ID : 84897608202

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