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このアイテムの引用には次の識別子を使用してください: http://hdl.handle.net/10119/16009

タイトル: Machine learning reveals orbital interaction in materials
著者: Pham, Tien Lam
Kino, Hiori
Terakura, Kiyoyuki
Miyake, Takashi
Tsuda, Koji
Takigawa, Ichigaku
Dam, Hieu Chi
キーワード: Materials Informatics
Machine learning
Data mining
発行日: 2017-10-26
出版者: Taylor and Francis
誌名: Science and Technology of Advanced Materials
巻: 18
号: 1
開始ページ: 756
終了ページ: 765
DOI: 10.1080/14686996.2017.1378060
抄録: 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 representationcan be highly useful in mining material data. Experimental investigation shows that the formationenergies of crystalline materials, atomization energies of molecular materials, and local magneticmoments of the constituent atoms in bimetal alloys of a lanthanide metal and transition-metal can be predicted with high accuracy using the OFM. Knowledge regarding the role of the coordinationnumbers 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 regressionanalyses using the OFM.
Rights: Tien Lam Pham, Hiori Kino, Kiyoyuki Terakura, Takashi Miyake, Koji Tsuda, Ichigaku Takigawa, and Hieu Chi Dam, Science and Technology of Advanced Materials, 18(1), 2017, 756-765. DOI:10.1080/14686996.2017.1378060. c 2017 The Author(s). Published by National Institute for Materials Science in partnership with Taylor & Francis. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
URI: http://hdl.handle.net/10119/16009
資料タイプ: publisher
出現コレクション:a10-1. 雑誌掲載論文 (Journal Articles)

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