Phys. Rev. Lett. 2017

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

Phys. Rev. Lett. 2017 txie-93/cgcnn

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights.

BAND GAP FORMATION ENERGY