Machine learning reveals orbital interaction in materials
Autor: | Tien Lam Pham, Hiori Kino, Kiyoyuki Terakura, Takashi Miyake, Koji Tsuda, Ichigaku Takigawa, Hieu Chi Dam |
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Jazyk: | angličtina |
Rok vydání: | 2017 |
Předmět: | |
Zdroj: | Science and Technology of Advanced Materials, Vol 18, Iss 1, Pp 756-765 (2017) |
Druh dokumentu: | article |
ISSN: | 1468-6996 1878-5514 14686996 |
DOI: | 10.1080/14686996.2017.1378060 |
Popis: | 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. |
Databáze: | Directory of Open Access Journals |
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