Equivarient Electron Density Predictions Accelerate Density Functional Theory Calculations

The Hohenberg-Kohn theorem formally maps the ground-state electron density of a many-electron system to the ground-state energy, laying the foundation of modern density functional theory (DFT). Practical applications of DFT start with an estimate charge density, such as the superposition of atomic densities (SAD), and iteratively solve the Kohn-Sham equations until self-consistency. We develop an E(3)-equivarient deep learning model to predict the self-consistent, ground-state electron density that outperform other models found in the literature such as DeepDFT for organic molecules and inorganic materials. Using these predicted electron densities as a starting point for DFT, we show that fewer self-consistent iterations are required to converge a DFT calculation, and that more calculations successfully converge compared to initializing with SAD. In addition, we show that non-self-consistent calculations using the predicted electron densities predict electronic and thermal properties of materials at near-DFT accuracy.



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