Chem. Mater. 2018

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

Chem. Mater. 2018 materialsvirtuallab/megnet

Similarly, we show that MEGNet models trained on ∼60, 000 crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps and elastic moduli of crystals, achieving better than DFT accuracy over a much larger data set.

DRUG DISCOVERY FORMATION ENERGY RELATIONAL REASONING