Generalizing Denoising to Non-Equilibrium Structures Improves
  Equivariant Force Fields
Yi-Lun Liao Tess Smidt Abhishek Das
Understanding the interactions of atoms such as forces in 3D atomistic
systems is fundamental to many applications like molecular dynamics and
catalyst design. However, simulating these interactions requires
compute-intensive ab initio calculations and thus results in limited data for
training neural networks. In this paper, we propose to use denoising
non-equilibrium structures (DeNS) as an auxiliary task to better leverage
training data and improve performance. For training with DeNS, we first corrupt
a 3D structure by adding noise to its 3D coordinates and then predict the
noise. Different from previous works on denoising, which are limited to
equilibrium structures, the proposed method generalizes denoising to a much
larger set of non-equilibrium structures. The main difference is that a
non-equilibrium structure does not correspond to local energy minima and has
non-zero forces, and therefore it can have many possible atomic positions
compared to an equilibrium structure. This makes denoising non-equilibrium
structures an ill-posed problem since the target of denoising is not uniquely
defined. Our key insight is to additionally encode the forces of the original
non-equilibrium structure to specify which non-equilibrium structure we are
denoising. Concretely, given a corrupted non-equilibrium structure and the
forces of the original one, we predict the non-equilibrium structure satisfying
the input forces instead of any arbitrary structures. Since DeNS requires
encoding forces, DeNS favors equivariant networks, which can easily incorporate
forces and other higher-order tensors in node embeddings. We study the
effectiveness of training equivariant networks with DeNS on OC20, OC22 and MD17
datasets and demonstrate that DeNS can achieve new state-of-the-art results on
OC20 and OC22 and significantly improve training efficiency on MD17.