Noncovalent Quantum Machine Learning Corrections to Density Functionals

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.