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Structure motif centric learning framework for inorganic crystalline systems
[article]
2020
arXiv
pre-print
Incorporation of physical principles in a network-based machine learning (ML) architecture is a fundamental step toward the continued development of artificial intelligence for materials science and condensed matter physics. In this work, as inspired by the Pauling rule, we propose that structure motifs (polyhedral formed by cations and surrounding anions) in inorganic crystals can serve as a central input to a machine learning framework for crystalline inorganic materials. Taking metal oxides
arXiv:2007.04145v1
fatcat:2gxto5p5tzfg5br7hadpkvmvna