On Representing Electronic Wave Functions with Sign Equivariant Neural Networks

arXiv:2403.05249v1 Announce Type: cross
Abstract: Recent neural networks demonstrated impressively accurate approximations of electronic ground-state wave functions. Such neural networks typically consist of a permutation-equivariant neural network followed by a permutation-antisymmetric operation to enforce the electronic exchange symmetry. While accurate, such neural networks are computationally expensive. In this work, we explore the flipped approach, where we first compute antisymmetric quantities based on the electronic coordinates and then apply sign equivariant neural networks to preserve the antisymmetry. While this approach promises …

abstract arxiv cs.lg electronic functions network networks neural network neural networks physics.chem-ph physics.comp-ph quant-ph state symmetry type work