  Modeling the energy and forces of atomic systems is a fundamental problem in
computational chemistry with the potential to help address many of the world's
most pressing problems, including those related to energy scarcity and climate
change. These calculations are traditionally performed using Density Functional
Theory, which is computationally very expensive. Machine learning has the
potential to dramatically improve the efficiency of these calculations from
days or hours to seconds. We propose the Spherical Channel Network (SCN) to
model atomic energies and forces. The SCN is a graph neural network where nodes
represent atoms and edges their neighboring atoms. The atom embeddings are a
set of spherical functions, called spherical channels, represented using
spherical harmonics. We demonstrate, that by rotating the embeddings based on
the 3D edge orientation, more information may be utilized while maintaining the
rotational equivariance of the messages. While equivariance is a desirable
property, we find that by relaxing this constraint in both message passing and
aggregation, improved accuracy may be achieved. We demonstrate state-of-the-art
results on the large-scale Open Catalyst dataset in both energy and force
prediction for numerous tasks and metrics.
