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In Journal of chemical theory and computation

We present non-covalent quantum machine learning corrections to six physically motivated density functionals with systematic errors. We demonstrate that the missing massively non-local and non-additive physical effects can be recovered by the quantum machine learning models. The models seamlessly account for various types of non-covalent 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 a 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.

Mezei Pal Daniel, van Lilienfeld Anatole

2020-Mar-04