Journal
SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS
Volume 18, Issue 1, Pages 756-765Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/14686996.2017.1378060
Keywords
Material descriptor; machine learning; data mining; magnetic materials; material informatics
Categories
Funding
- Japan Science and Technology Agency (JST)
- MEXT
- Materials research by Information Integration' Initiative (MI2 I) project of the Support Program for Starting Up Innovation Hub from Japan Science and Technology Agency (JST)
- JSPS KAKENHI [17K19953, 17H01783]
- Grants-in-Aid for Scientific Research [17H01783, 17K19953] Funding Source: KAKEN
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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. [GRAPHICS]
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