Classical intermolecular potentials typically require an extensive
parametrization procedure for any new compound considered. To do away with
prior parametrization, we propose a combination of physics-based potentials
with machine learning (ML), coined IPML, which is transferable across small
neutral organic and biologically-relevant molecules. ML models provide
on-the-fly predictions for environment-dependent local atomic properties:
electrostatic multipole coefficients (significant error reduction compared to
previously reported), the population and decay rate of valence atomic
densities, and polarizabilities across conformations and chemical compositions
of H, C, N, and O atoms. These parameters enable accurate calculations of
intermolecular contributions---electrostatics, charge penetration, repulsion,
induction/polarization, and many-body dispersion. Unlike other potentials, this
model is transferable in its ability to handle new molecules and conformations
without explicit prior parametrization: All local atomic properties are
predicted from ML, leaving only eight global parameters---optimized once and
for all across compounds. We validate IPML on various gas-phase dimers at and
away from equilibrium separation, where we obtain mean absolute errors between
0.4 and 0.7 kcal/mol for several chemically and conformationally diverse
datasets representative of non-covalent interactions in biologically-relevant
molecules. We further focus on hydrogen-bonded complexes---essential but
challenging due to their directional nature---where datasets of DNA base pairs
and amino acids yield an extremely encouraging 1.4 kcal/mol error. Finally, and
as a first look, we consider IPML in denser systems: water clusters,
supramolecular host-guest complexes, and the benzene crystal.Comment: 15 pages, 9 figure