Orbital Graph Convolutional Neural Network for Material Property Prediction

Understand

Material representations that are compatible with machine learning models play a key role in developing models that exhibit high accuracy for property prediction.

• Atomic orbital interactions are one of the important factors that govern the properties of crystalline materials, from which the local chemical environments of atoms is inferred.
• Therefore, to develop robust machine learningmodels for material properties prediction, it is imperative to include features representing such chemical attributes.
• Here, we propose the Orbital Graph Convolutional Neural Network (OGCNN), a crystal graph convolutional neural network framework that includes atomic orbital interaction features that learns material properties in a robust way.