Kohn-Sham density functional theory (DFT) is the standard method for first-principles calculations in computational chemistry and materials science. More accurate theories such as the random-phase approximation (RPA) are limited in application due to their large computational cost. Here, we construct a DFT substitute functional for the RPA using supervised and unsupervised machine learning (ML) techniques. Our ML-RPA model can be interpreted as a non-local extension to the standard gradient approximation. We train an ML-RPA functional for diamond surfaces and liquid water and show that ML-RPA can outperform the standard gradient functionals in terms of accuracy. Our work demonstrates how ML-RPA can extend the applicability of the RPA to larger system sizes, time scales and chemical spaces.
@article{arxiv.2308.00665,
title = {Machine learning density functionals from the random-phase approximation},
author = {Stefan Riemelmoser and Carla Verdi and Merzuk Kaltak and Georg Kresse},
journal= {arXiv preprint arXiv:2308.00665},
year = {2023}
}