Learning Force Fields using Covariant Compositional Networks

Deep neural networks have emerged as a powerful technique for augmenting molecular dynamics simulations. Recent work from our group has introduced the concept of a Covariant Compositional Network, a neural network architecture that by construction respects the symmetries of underlying training data. These networks were shown to be competitive with recent deep learning techniques in quantum chemistry, including Google Brain. In this talk, we generalize these networks to learn potential energy surfaces and their corresponding force fields. Our technique produces highly accurate representations of molecular force fields. More generally we have a technique for developing a highly accurate universal force field for atomistic simulations.