Unifying O(3) equivariant neural networks design with tensor-network formalism
Zimu Li,
Zihan Pengmei,
Han Zheng
et al.
Abstract:Many learning tasks, including learning potential energy surfaces from ab initio calculations, involve global spatial symmetries and permutational symmetry between atoms or general particles. Equivariant graph neural networks are a standard approach to such problems, with one of the most successful methods employing tensor products between various tensors that transform under the spatial group. However, as the number of different tensors and the complexity of relationships between them increase, maintaining p… Show more
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