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

ICLR 2026 Conference Submission19051 Authors

Keywords: Graph Neural Network, Electron Density, Density Functional Theory, Message Passing, Basis Overlap, Equivariance, Molecules

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 result 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 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.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 19051