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

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.

Published in Nature Computational Science, March 2024. Full paper with supplementary information

References in corpus (6)

• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Pure density functional for strong correlations and the thermodynamic limit from machine learning
• Order-$N$ orbital-free density-functional calculations with machine learning of functional derivatives for semiconductors and metals
• Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning
• KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory
• Symmetry- and Gradient-enhanced Gaussian Process Regression for the Active Learning of Potential Energy Surfaces in Porous Materials

Cited by in corpus (5)

• AI-driven inverse design of materials: Past, present and future
• Multi-channel machine learning based nonlocal kinetic energy density functional for semiconductors
• Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory
• Leveraging Normalizing Flows for Orbital-Free Density Functional Theory
• Solving deep-learning density functional theory via variational autoencoders