論文

査読有り
2022年2月

Chemical Composition Data-Driven Machine-Learning Prediction for Phase Stability and Materials Properties of Inorganic Crystalline Solids

PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS
  • Taruto Atsumi
  • ,
  • Kosei Sato
  • ,
  • Yudai Yamaguchi
  • ,
  • Masato Hamaie
  • ,
  • Risa Yasuda
  • ,
  • Naoto Tanibata
  • ,
  • Hayami Takeda
  • ,
  • Masanobu Nakayama
  • ,
  • Masayuki Karasuyama
  • ,
  • Ichiro Takeuchi

記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1002/pssb.202100525
出版者・発行元
WILEY-V C H VERLAG GMBH

Materials informatics has attracted significant attention toward the efficient discovery and development of new functional materials. Machine-learning regression techniques have often been used to establish a link between properties. In this respect, precise and rational regression is contingent on the choice of descriptors used to represent both composition and structural information. The usage of structure-derived descriptors restricts the prediction range to the registered materials in the crystal structure database owing to the structural information requirements. Conversely, machine-learning regression based only on compositional descriptors is free from this restriction, despite the fact that the prediction performance may diminish. Herein, their prediction performance is improved using compositional descriptors with histograms, and detailed surveys are performed on their ability to extrapolate. The proposed model achieves a high prediction accuracy based on the area under the receiver operating characteristic (ROC) curve (specifically, AUC > 0.9).

リンク情報
DOI
https://doi.org/10.1002/pssb.202100525
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000755669200001&DestApp=WOS_CPL
ID情報
  • DOI : 10.1002/pssb.202100525
  • ISSN : 0370-1972
  • eISSN : 1521-3951
  • Web of Science ID : WOS:000755669200001

エクスポート
BibTeX RIS