Quantum chemical accuracy from density functional approximations via machine learning.

ABSTRACT: Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal ? mol^-1 with presently-available functionals. Ab initio methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. In this paper, we leverage machine learning to calculate coupled-cluster energies from DFT densities, reaching quantum chemical accuracy (errors below 1 kcal ? mol^-1) on test data. Moreover, density-based ?-learning (learning only the correction to a standard DFT calculation, termed ?-DFT ) significantly reduces the amount of training data required, particularly when molecular symmetries are included. The robustness of ?-DFT  is highlighted by correcting "on the fly" DFT-based molecular dynamics (MD) simulations of resorcinol (C₆H₄(OH)₂) to obtain MD trajectories with coupled-cluster accuracy. We conclude, therefore, that ?-DFT  facilitates running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT fails.

SUBMITTER: Bogojeski M 

PROVIDER: S-EPMC7567867 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature