Molecular mechanics (MM) potentials have long been a workhorse of
computational chemistry. Leveraging accuracy and speed, these functional forms
find use in a wide variety of applications from rapid virtual screening to
detailed free energy calculations. Traditionally, MM potentials have relied on
human-curated, inflexible, and poorly extensible discrete chemical perception
rules (atom types) for applying parameters to molecules or biopolymers, making
them difficult to optimize to fit quantum chemical or physical property data.
Here, we propose an alternative approach that uses graph nets to perceive
chemical environments, producing continuous atom embeddings from which valence
and nonbonded parameters can be predicted using a feed-forward neural network.
Since all stages are built using smooth functions, the entire process of
chemical perception and parameter assignment is differentiable end-to-end with
respect to model parameters, allowing new force fields to be easily
constructed, extended, and applied to arbitrary molecules. We show that this
approach has the capacity to reproduce legacy atom types and can be fit to MM
and QM energies and forces, among other targets