On Representing Electronic Wave Functions with Sign Equivariant Neural Networks
This work addresses computational efficiency in quantum chemistry simulations, but is incremental as it shows no advantages over existing methods.
The paper tackled the problem of efficiently representing electronic ground-state wave functions by exploring a flipped approach using sign equivariant neural networks, but found it reduced to a common Jastrow factor with little to no improvements over baselines.
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 acceleration thanks to the lower-dimensional representation, we demonstrate that it reduces to a Jastrow factor, a commonly used permutation-invariant multiplicative factor in the wave function. Our empirical results support this further, finding little to no improvements over baselines. We conclude with neither theoretical nor empirical advantages of sign equivariant functions for representing electronic wave functions within the evaluation of this work.