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

Phys. Rev. Lett. 2017 Tian Xie Jeffrey C. Grossman

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. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials... (read more)

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  • MATERIALS SCIENCE