A Function-Centric Graph Neural Network Approach for Predicting Electron Densities

Abstract

Electronic structure predictions are relevant for a wide range of applications, from drug discovery to materials science. Since the cost of purely quantum mechanical methods can be prohibitive, machine learning surrogates are used to predict the results of these calculations. This work introduces the Basis Overlap Architecture (BOA), an equivariant graph neural network architecture based on a novel message passing scheme that utilizes the overlap matrix of the basis functions used to represent the predicted ground state electron density. BOA is evaluated on QM9 and MD density datasets, surpassing the previous state of the art in predicting accurate electron densities. Excellent generalization to larger molecules of up to nearly 200 atoms is demonstrated using a model trained only on QM9 molecules of at most 9 heavy atoms.