Background and goal
Espaloma trains a graph neural network (GNN) to generate values for the traditional parameters within a molecular dynamics force field: bond lengths, force constants, etc. It can generate both bonded and non-bonded terms, such as bonds, angles, dihedrals, impropers, partial charges, and Lennard Jones parameters. It has shown good performance on free energy benchmarks.
While OpenFF has yet to move to a full neural network force field in the framework of Espaloma, it may be useful to use Espaloma as a reference, and we may be able to use Espaloma to determine areas where OpenFF parameters need improvement. For example, there may be cases where OpenFF uses one parameter to encode a particular chemistry, that Espaloma splits into many different values. If assigned parameter values are significantly different between Espaloma and OpenFF, that would also be worth exploring.