  The calculation of electron density distribution using density functional
theory (DFT) in materials and molecules is central to the study of their
quantum and macro-scale properties, yet accurate and efficient calculation
remains a long-standing challenge. We introduce ChargE3Net, an E(3)-equivariant
graph neural network for predicting electron density in atomic systems.
ChargE3Net enables the learning of higher-order equivariant feature to achieve
high predictive accuracy and model expressivity. We show that ChargE3Net
exceeds the performance of prior work on diverse sets of molecules and
materials. When trained on the massive dataset of over 100K materials in the
Materials Project database, our model is able to capture the complexity and
variability in the data, leading to a significant 26.7% reduction in
self-consistent iterations when used to initialize DFT calculations on unseen
materials. Furthermore, we show that non-self-consistent DFT calculations using
our predicted charge densities yield near-DFT performance on electronic and
thermodynamic property prediction at a fraction of the computational cost.
Further analysis attributes the greater predictive accuracy to improved
modeling of systems with high angular variations. These results illuminate a
pathway towards a machine learning-accelerated ab initio calculations for
materials discovery.
