Efficient prediction of 3D electron densities using machine learning

Abstract

The Kohn-Sham scheme of density functional theory is one of the most widely used methods to solve electronic structure problems for a vast variety of atomistic systems across different scientific fields. While the method is fast relative to other first principles methods and widely successful, the computational time needed is still not negligible, making it difficult to perform calculations for very large systems or over long time-scales. In this submission, we revisit a machine learning model capable of learning the electron density and the corresponding energy functional based on a set of training examples. It allows us to bypass solving the Kohn-Sham equations, providing a significant decrease in computation time. We specifically focus on the machine learning formulation of the Hohenberg-Kohn map and its decomposability. We give results and discuss challenges, limits and future directions.

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Cited by in corpus (4)

• Bayesian optimization for tuning and selecting hybrid-density functionals
• Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
• Gaussian Process-Based Refinement of Dispersion Corrections
• Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks