2017年
Machine learning reveals orbital interaction in materials.
Science and technology of advanced materials
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- 巻
- 18
- 号
- 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
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- 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
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- 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