Materials graph networks as a universal machine learning framework for molecules and crystals

Chi has recently developed MatErials Graph Networks (MEGNet) based on DeepMind’s graph networks approach.We show that MEGNet models are a universal approach to machine learning for both crystals and molecules, outperforming prior ML models on a broad array of properties. We also demonstrate the incorporation of state (e.g., temperature, pressure) into MEGNet models, and how transfer learning can be used to accelerate and improve the accuracy of models trained on smaller data sets.

A preprint of our paper is published on arXiv and the models are available at We have also posted a useful data set of 69,000 crystals from the Materials Project on figshare for ML purposes.