  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.
