論文

査読有り 最終著者 責任著者 国際誌
2017年

Machine learning reveals orbital interaction in materials.

Science and technology of advanced materials
  • Tien Lam Pham
  • ,
  • Hiori Kino
  • ,
  • Kiyoyuki Terakura
  • ,
  • Takashi Miyake
  • ,
  • Koji Tsuda
  • ,
  • Ichigaku Takigawa
  • ,
  • Hieu Chi Dam

18
1
開始ページ
756
終了ページ
765
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1080/14686996.2017.1378060
出版者・発行元
TAYLOR & FRANCIS LTD

We propose a novel representation of materials named an 'orbital-field matrix (OFM)', which is based on the distribution of valence shell electrons. We demonstrate that this new representation can be highly useful in mining material data. Experimental investigation shows that the formation energies of crystalline materials, atomization energies of molecular materials, and local magnetic moments of the constituent atoms in bimetal alloys of lanthanide metal and transition-metal can be predicted with high accuracy using the OFM. Knowledge regarding the role of the coordination numbers of the transition-metal and lanthanide elements in determining the local magnetic moments of the transition-metal sites can be acquired directly from decision tree regression analyses using the OFM.

リンク情報
DOI
https://doi.org/10.1080/14686996.2017.1378060
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/29152012
PubMed Central
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5678453
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000417600000001&DestApp=WOS_CPL
URL
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85032589753&origin=inward
ID情報
  • DOI : 10.1080/14686996.2017.1378060
  • ISSN : 1468-6996
  • eISSN : 1878-5514
  • PubMed ID : 29152012
  • PubMed Central 記事ID : PMC5678453
  • SCOPUS ID : 85032589753
  • Web of Science ID : WOS:000417600000001

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