CONI-Net: Machine Learning of Separable Intermolecular Force Fields

Noncovalent interactions
(NCIs) play an essential role in soft
matter and biomolecular simulations. The ab initio method symmetry-adapted
perturbation theory allows a precise quantitative analysis of NCIs
and offers an inherent energy decomposition, enabling a deeper understanding
of the nature of intermolecular interactions. However, this method
is limited to small systems, for instance, dimers of molecules. Here,
we present a scale-bridging approach to systematically derive an intermolecular
force field from ab initio data while preserving the energy decomposition
of the underlying method. We apply the model in molecular dynamics
simulations of several solvents and compare two predicted thermodynamic
observablesmass density and enthalpy of vaporizationto
experiments and established force fields. For a data set limited to
hydrocarbons, we investigate the extrapolation capabilities to molecules
absent from the training set. Overall, despite the affordable moderate
quality of the reference ab initio data, we find promising results.
With the straightforward data set generation procedure and the lack
of target data in the fitting process, we have developed a method
that enables the rapid development of predictive force fields with
an extra dimension of insights into the balance of NCIs.