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Materials Screening

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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 MATERIALS SCREENING TOTAL MAGNETIZATION

Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors

15 May 2019peterbjorgensen/msgnet

The possibilities for prediction in a realistic computational screening setting is investigated on a dataset of 5976 ABSe$_3$ selenides with very limited overlap with the OQMD training set.

FORMATION ENERGY MATERIALS SCREENING