The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials

• URL: http://arxiv.org/abs/2205.06643v1
• Date: Fri, 13 May 2022 13:47:12 GMT
• Title: The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
• Abstract summary: NequIP is a message passing neural network with equivariant features that showed state of the art accuracy. We construct a mathematical framework that unifies these models. We present BOTNet, a much-simplified version of NequIP, which has an interpretable architecture and maintains accuracy on benchmark datasets.
• Score: 3.864610688022996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures. Particularly notable among these are the Atomic Cluster Expansion (ACE), which unified many of the earlier ideas around atom density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message passing neural network with equivariant features that showed state of the art accuracy. In this work, we construct a mathematical framework that unifies these models: ACE is generalised so that it can be recast as one layer of a multi-layer architecture. From another point of view, the linearised version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in the unified design space. We demonstrate this by an ablation study of NequIP via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, and shed some light on which design choices are critical for achieving high accuracy. Finally, we present BOTNet (Body-Ordered-Tensor-Network), a much-simplified version of NequIP, which has an interpretable architecture and maintains accuracy on benchmark datasets.

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