  We report a new deep learning message passing network that takes inspiration
from Newton's equations of motion to learn interatomic potentials and forces.
With the advantage of directional information from trainable latent force
vectors, and physics-infused operators that are inspired by the Newtonian
physics, the entire model remains rotationally equivariant, and many-body
interactions are inferred by more interpretable physical features. We test
NewtonNet on the prediction of several reactive and non-reactive high quality
ab initio data sets including single small molecule dynamics, a large set of
chemically diverse molecules, and methane and hydrogen combustion reactions,
achieving state-of-the-art test performance on energies and forces with far
greater data and computational efficiency than other deep learning models.
