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. Here, Trevor Gokey’s work on automated parameter generation could come in handy for partitioning espaloma data. If assigned parameter values are significantly different between Espaloma and OpenFF, that would also be worth exploring.