Machine learning reveals orbital interaction in crystalline materials

2017/05/02 by Tien Lam Pham, Pham, Tien Lam, Hiori Kino +12
Materials Science · Physics and Astronomy · #Advanced Chemical Physics Studies #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #X-ray Diffraction in Crystallography #cond-mat.mtrl-sci

paper · pdf · doi:10.48550/arxiv.1705.01043

10 pages

openalex publication_date 2017/05/02 · arxiv created 2017/05/03 · arxiv updated 2017/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose a novel representation of crystalline materials named orbital-field matrix (OFM) based on the distribution of valence shell electrons. We demonstrate that this new representation can be highly useful in mining material data. Our experiment shows that the formation energies of crystalline materials, the atomization energies of molecular materials, and the local magnetic moments of the constituent atoms in transition metal--rare-earth metal bimetal alloys can be predicted with high accuracy using the OFM. Knowledge regarding the role of coordination numbers of transition-metal and rare-earth metal elements in determining the local magnetic moment of transition metal sites can be acquired directly from decision tree regression analyses using the OFM.

Citations

• Projector augmented-wave method
• Efficient iterative schemes forab initiototal-energy calculations using a plane-wave basis set

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