  Progress towards the energy breakthroughs needed to combat climate change can
be significantly accelerated through the efficient simulation of atomic
systems. Simulation techniques based on first principles, such as Density
Functional Theory (DFT), are limited in their practical use due to their high
computational expense. Machine learning approaches have the potential to
approximate DFT in a computationally efficient manner, which could dramatically
increase the impact of computational simulations on real-world problems.
Approximating DFT poses several challenges. These include accurately modeling
the subtle changes in the relative positions and angles between atoms, and
enforcing constraints such as rotation invariance or energy conservation. We
introduce a novel approach to modeling angular information between sets of
neighboring atoms in a graph neural network. Rotation invariance is achieved
for the network's edge messages through the use of a per-edge local coordinate
frame and a novel spin convolution over the remaining degree of freedom. Two
model variants are proposed for the applications of structure relaxation and
molecular dynamics. State-of-the-art results are demonstrated on the
large-scale Open Catalyst 2020 dataset. Comparisons are also performed on the
MD17 and QM9 datasets.
