Quantum chemical accuracy from density functional approximations via machine learning

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 Delta -learning (learning only the correction to a standard DFT calculation, termed Delta -DFT ) significantly reduces the amount of training data required, particularly when molecular symmetries are included. The robustness of Delta -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 Delta -DFT facilitates running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT fails. High-level ab initio quantum chemical methods carry a high computational burden, thus limiting their applicability. Here, the authors employ machine learning to generate coupled-cluster energies and forces at chemical accuracy for geometry optimization and molecular dynamics from DFT densities.