Molecular Potentials

Graph Neural Networks accurately approximate the potential energy surfaces of molecular systems, but the learned potentials are hard to trace back to the underlying physical interactions. This talk presents an approach that builds interpretable parameters directly into the architecture, so that only the relevant interactions survive training, revealing which chemical species interact, at which spatial scale and with which many-body contributions, and finally distilling the network into a transparent analytical potential.