Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning

TL;DR

This paper introduces M-OFDFT, a deep learning-based orbital-free density functional theory method that achieves Kohn-Sham DFT accuracy for molecules and scales efficiently to larger systems, advancing quantum chemistry simulations.

Contribution

The paper presents a novel deep learning functional model for OFDFT that incorporates non-locality and achieves high accuracy and good extrapolation for large molecular systems.

Findings

M-OFDFT matches Kohn-Sham DFT accuracy on various molecules.

M-OFDFT extrapolates well to larger molecules beyond training data.

The method offers improved scaling for large molecular systems.

Abstract

Orbital-free density functional theory (OFDFT) is a quantum chemistry formulation that has a lower cost scaling than the prevailing Kohn-Sham DFT, which is increasingly desired for contemporary molecular research. However, its accuracy is limited by the kinetic energy density functional, which is notoriously hard to approximate for non-periodic molecular systems. Here we propose M-OFDFT, an OFDFT approach capable of solving molecular systems using a deep learning functional model. We build the essential non-locality into the model, which is made affordable by the concise density representation as expansion coefficients under an atomic basis. With techniques to address unconventional learning challenges therein, M-OFDFT achieves a comparable accuracy with Kohn-Sham DFT on a wide range of molecules untouched by OFDFT before. More attractively, M-OFDFT extrapolates well to molecules much larger than those seen in training, which unleashes the appealing scaling of OFDFT for studying large molecules including proteins, representing an advancement of the accuracy-efficiency trade-off frontier in quantum chemistry.