Neural-Network Correlation Functions for Light Nuclei with Chiral Two- and Three-Body Interactions

TL;DR

This paper introduces neural network-based wave functions for light nuclei in quantum Monte Carlo, significantly improving energy estimates and capturing complex many-body correlations efficiently.

Contribution

It presents a novel neural network approach that effectively models many-body correlations in light nuclei, achieving near GFMC accuracy with reduced computational effort.

Findings

Achieves 91% improvement over standard variational Monte Carlo

Reaches within 0.45% of GFMC ground state energy for 3H

Successfully incorporates three-body interactions in neural network wave functions

Abstract

Finding high-quality trial wave functions for quantum Monte Carlo calculations of light nuclei requires a strong intuition for modeling the interparticle correlations as well as large computational resources for exploring the space of variational parameters. Moreover, for systems with three-body interactions, the wave function should account for many-body effects beyond simple pairwise correlations. In this work, we design neural networks that efficiently incorporate these factors to generate expressive wave function Ansatzes for light nuclei using variational Monte Carlo. Our neural-network approach for A=3 nuclei can capture, already at the level of variational Monte Carlo, the overwhelming majority of the ground-state energy estimated by Green's Function Monte Carlo (GFMC). We can find a 91% improvement over standard variational Monte Carlo and achieve a ground state energy within 0.45% of the GFMC result for 3H using the softest chiral interaction with neural networks. The result indicates the potential of neural networks to construct effective trial wave functions for quantum Monte Carlo calculations.