  This work presents Neural Equivariant Interatomic Potentials (NequIP), a
SE(3)-equivariant neural network approach for learning interatomic potentials
from ab-initio calculations for molecular dynamics simulations. While most
contemporary symmetry-aware models use invariant convolutions and only act on
scalars, NequIP employs SE(3)-equivariant convolutions for interactions of
geometric tensors, resulting in a more information-rich and faithful
representation of atomic environments. The method achieves state-of-the-art
accuracy on a challenging set of diverse molecules and materials while
exhibiting remarkable data efficiency. NequIP outperforms existing models with
up to three orders of magnitude fewer training data, challenging the widely
held belief that deep neural networks require massive training sets. The high
data efficiency of the method allows for the construction of accurate
potentials using high-order quantum chemical level of theory as reference and
enables high-fidelity molecular dynamics simulations over long time scales.
