E（3）-等变原子心原子间势的设计空间
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
摘要：
在过去的几年里，机器学习原子间相互作用势的快速发展产生了许多新的架构。其中特别值得注意的是原子簇扩展（ACE），它统一了许多早期的想法，围绕原子密度为基础的描述符，和神经等变原子间电位（NequIP），一个消息传递神经网络与等变功能，显示出最先进的准确性。在这项工作中，我们构建了一个数学框架，统一这些模型：ACE是广义的，使它可以被重铸为一个多层架构的一层。从另一个角度来看，NequIP的线性化版本被理解为一个更大的多项式模型的特定稀疏化。我们的框架还提供了一个实用的工具，系统地探讨不同的选择，在统一的设计空间。我们通过一组实验对NequIP进行消融研究来证明这一点，这些实验研究了域内和域外的准确性以及距离训练数据很远的平滑外推，并阐明了哪些设计选择对于实现高准确性至关重要。最后，我们提出了BOTNet（Body-Ordered-Tensor-Network），这是NequIP的一个简化版本，它具有可解释的架构，并在基准数据集上保持准确性。
英文摘要：
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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