Higher-Order Equivariant Neural Networks for Charge Density Prediction
  in Materials
Teddy Koker Keegan Quigley Eric Taw Kevin Tibbetts Lin Li
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 in the field of material science. This work
introduces ChargE3Net, an E(3)-equivariant graph neural network for predicting
electron density in atomic systems. ChargE3Net achieves equivariance through
the use of higher-order tensor representations, and directly predicts the
charge density at any arbitrary point in the system. We show that our method
achieves greater performance than prior work on large and diverse sets of
molecules and materials, and scales to larger systems than what is feasible to
compute with DFT. Using predicted electron densities as an initialization, we
show that fewer self-consistent iterations are required to converge DFT over
the default initialization. In addition, we show that non-self-consistent
calculations using the predicted electron densities can predict electronic and
thermodynamic properties of materials at near-DFT accuracy.