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Total Magnetization

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Crystal Graph Neural Networks for Data Mining in Materials Science

Technical report, RIMCS LLC 2019 Tony-Y/cgnn

This paper proposes crystal graph neural networks (CGNNs) that use no bond distances, and introduces a scale-invariant graph coordinator that makes up crystal graphs for the CGNN models to be trained on the dataset based on a theoretical materials database.

BAND GAP FORMATION ENERGY GRAPH NEURAL NETWORK MATERIALS SCREENING TOTAL MAGNETIZATION