  With massive amounts of atomic simulation data available, there is a huge
opportunity to develop fast and accurate machine learning models to approximate
expensive physics-based calculations. The key quantity to estimate is atomic
forces, where the state-of-the-art Graph Neural Networks (GNNs) explicitly
enforce basic physical constraints such as rotation-covariance. However, to
strictly satisfy the physical constraints, existing models have to make
tradeoffs between computational efficiency and model expressiveness. Here we
explore an alternative approach. By not imposing explicit physical constraints,
we can flexibly design expressive models while maintaining their computational
efficiency. Physical constraints are implicitly imposed by training the models
using physics-based data augmentation. To evaluate the approach, we carefully
design a scalable and expressive GNN model, ForceNet, and apply it to OC20
(Chanussot et al., 2020), an unprecedentedly-large dataset of quantum physics
calculations. Our proposed ForceNet is able to predict atomic forces more
accurately than state-of-the-art physics-based GNNs while being faster both in
training and inference. Overall, our promising and counter-intuitive results
open up an exciting avenue for future research.
