Machine learning reveals orbital interaction in materials. Issue 1 (31st December 2017)

• Record Type: Journal Article
• Authors: Lam Pham, Tien; Kino, Hiori; Terakura, Kiyoyuki; Miyake, Takashi; Tsuda, Koji; Takigawa, Ichigaku; Chi Dam, Hieu
• Is Part Of: Science and technology of advanced materials. Volume 18:Issue 1(2017)
• Page Start: 756
• Page End: 765
• Publication Date: 2017-12-31
• Subjects: Material descriptor -- machine learning -- data mining -- magnetic materials -- material informatics; 60 New topics/Others -- 404 Materials informatics / Genomics -- 203 Magnetics / Spintronics / Superconductors; Materials -- Technological innovations -- Periodicals; 620.112
• DOI: 10.1080/14686996.2017.1378060
• Languages: English
• ISSNs: 1468-6996

Abstract

Abstract : 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. Graphical Abstract: