This work presents Neural Equivariant Interatomic Potentials (NequIP), an
E(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 E(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 and diverse set of 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.

这项工作提出了神经等变分子间势 (NequIP), 一种用于学习分子动力学模拟的从自由计算中学习分子间势的 E (3) 等变神经网络方法。NequIP 通过几何张量相互作用利用 E (3)- 等变卷积，此方法在数据效率方面具有非凡的性能，使得准确利用高损耗的量子化学理论构建准确潜力成为可能，并使得长时间尺度上的高保障分子动力学模拟成为可能。