Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry

Significance The interplay between molecular simulation and artificial intelligence has spurred many insights into chemical discovery, yet the data requirement of machine learning approaches remains a bottleneck. We tackle this challenge by developing a rigorous framework to conserve the symmetries of quantum chemical systems within deep neural networks. Our framework greatly improves the prediction of numerous molecular electronic and energetic properties, even for systems far from the training data. Through tight integration of quantum theory, geometry, and machine learning, our study offers a solution to vastly accelerate the in silico modelling of chemical phenomena in many domains, such as material design and catalysis.