Machine Learning Density Functionals from the Random-Phase Approximation

Author(s)
Stefan Riemelmoser, Carla Verdi, Merzuk Kaltak, Georg Kresse
Abstract

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 use machine learning to map the RPA to a pure Kohn-Sham density functional. The machine learned RPA model (ML-RPA) is a nonlocal extension of the standard gradient approximation. The density descriptors used as ingredients for the enhancement factor are nonlocal counterparts of the local density and its gradient. Rather than fitting only RPA exchange-correlation energies, we also include derivative information in the form of RPA optimized effective potentials. We train a single ML-RPA functional for diamond, its surfaces, and liquid water. The accuracy of ML-RPA for the formation energies of 28 diamond surfaces reaches that of state-of-the-art van der Waals functionals. For liquid water, however, ML-RPA cannot yet improve upon the standard gradient approximation. Overall, our work demonstrates how machine learning can extend the applicability of the RPA to larger system sizes, time scales, and chemical spaces.

Organisation(s)
Computational Materials Physics
External organisation(s)
The University of Sydney, University of Queensland, VASP Software GmbH
Journal
Journal of Chemical Theory and Computation
Volume
19
Pages
7287-7299
No. of pages
13
ISSN
1549-9618
DOI
https://doi.org/10.48550/arXiv.2308.00665
Publication date
10-2023
Peer reviewed
Yes
Austrian Fields of Science 2012
103018 Materials physics, 104027 Computational chemistry, 102019 Machine learning
ASJC Scopus subject areas
Computer Science Applications, Physical and Theoretical Chemistry
Portal url
https://ucris.univie.ac.at/portal/en/publications/machine-learning-density-functionals-from-the-randomphase-approximation(d246bb06-be14-470c-8b34-49510baba2e3).html