  Kohn-Sham density functional theory (KS-DFT) has found widespread application
in accurate electronic structure calculations. However, it can be
computationally demanding especially for large-scale simulations, motivating
recent efforts toward its machine-learning (ML) acceleration. We propose a
neural network self-consistent fields (NeuralSCF) framework that establishes
the Kohn-Sham density map as a deep learning objective, which encodes the
mechanics of the Kohn-Sham equations. Modeling this map with an
SE(3)-equivariant graph transformer, NeuralSCF emulates the Kohn-Sham
self-consistent iterations to obtain electron densities, from which other
properties can be derived. NeuralSCF achieves state-of-the-art accuracy in
electron density prediction and derived properties, featuring exceptional
zero-shot generalization to a remarkable range of out-of-distribution systems.
NeuralSCF reveals that learning from KS-DFT's intrinsic mechanics significantly
enhances the model's accuracy and transferability, offering a promising
stepping stone for accelerating electronic structure calculations through
mechanics learning.
