# Non-covalent quantum machine learning corrections to density functionals

## TL;DR

This paper introduces noncovalent quantum machine learning corrections to density functionals, effectively capturing nonlocal effects and improving predictions of molecular interactions, especially in complex systems like water clusters and hydrogen bonds.

## Contribution

It develops a machine learning correction method that enhances density functional accuracy by modeling nonlocal and nonadditive effects, surpassing traditional dispersion corrections.

## Key findings

- Improves dissociation curve predictions for molecules.
- Accurately models two- and three-body interactions in water clusters.
- Outperforms standard dispersion corrections in hydrogen bonding cases.

## Abstract

We present noncovalent quantum machine learning corrections to six physically motivated density functionals with systematic errors. We demonstrate that the missing massively nonlocal and nonadditive physical effects can be recovered by quantum machine learning models. The models seamlessly account for various types of noncovalent interactions and enable accurate predictions of dissociation curves. The correction improves the description of molecular two- and three-body interactions crucial in large water clusters and provides a reasonable atomic-resolution picture about the interaction energy errors of approximate density functionals that can be useful information in the development of more accurate density functionals. We show that given sufficient training instances the correction is more flexible than standard molecular mechanical dispersion corrections, and thus it can be applied for cases where many dispersion corrected density functionals fail, such as hydrogen bonding.