Year: 2025
Author: Lizhong Fu, Honghui Shang, Jinlong Yang
Communications in Computational Chemistry, Vol. 7 (2025), Iss. 2 : pp. 120–126
Abstract
Neural network quantum states represent a powerful approach for solving electronic structures in strongly correlated molecular and material systems. For a neural network ansatz to be accurate, it must effectively learn the phase of a complex wave function. In this work, we demonstrate several different network structures as the phase network for a Transformer-based neural network quantum state implementation. We compare the accuracy of ground state energies, the number of parameters, and computational time across several small molecules. Furthermore, we propose a phase network setup that combines cross attention and multilayer perceptron structures, with the number of parameters remaining constant across different systems. Such an architecture may help reduce computational costs and enable transfer learning to larger quantum chemical systems.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/cicc.2025.92.01
Communications in Computational Chemistry, Vol. 7 (2025), Iss. 2 : pp. 120–126
Published online: 2025-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 7
Keywords: neural network quantum states phase network electronic structure calculation.