Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions

Simulations of molecular
systems using electronic structure methods
are still not feasible for many systems of biological importance.
As a result, empirical methods such as force fields (FF) have become
an established tool for the simulation of large and complex molecular
systems. The parametrization of FF is, however, time-consuming and
has traditionally been based on experimental data. Recent years have
therefore seen increasing efforts to automatize FF parametrization
or to replace FF with machine-learning (ML) based potentials. Here,
we propose an alternative strategy to parametrize FF, which makes
use of ML and gradient-descent based optimization while retaining
a functional form founded in physics. Using a predefined functional
form is shown to enable interpretability, robustness, and efficient
simulations of large systems over long time scales. To demonstrate
the strength of the proposed method, a fixed-charge and a polarizable
model are trained on ab initio potential-energy surfaces.
Given only information about the constituting elements, the molecular
topology, and reference potential energies, the models successfully
learn to assign atom types and corresponding FF parameters from scratch.
The resulting models and parameters are validated on a wide range
of experimentally and computationally derived properties of systems
including dimers, pure liquids, and molecular crystals.