Equivariant Interatomic Potentials

Symmetry plays a central role in the representation of materials for the purpose of Machine Learning. In particular, all sensible representations must obey the symmetries of 3D space: translation, rotation, and inversion, in addition to permutation symmetry with respect to the labeling of atoms. Traditionally, representations have been constructed to possess invariance with respect to the above transformations. In this talk, I will discuss recent efforts to generalize invariance to the broader class of equivariant representations and demonstrate how this leads to a large increase in generalization accuracy and sample-efficiency of the learned models. The talk will then discuss the recently introduced Neural Equivariant Interatomic Potential (NequIP), an E(3)-equivariant Interatomic Potential that exhibits unprecedented accuracy and sample efficiency and outperforms invariant potentials with up to 1000x fewer reference data. I will discuss applications of NequIP to a diverse set of materials systems, including Li diffusion, amorphous structures, heterogeneous catalysis, and water. Finally, I will discuss our current theoretical understanding of the role of equivariance and explore connections with existing approaches, such as the atomic cluster expansion.