Local Pseudopotential Unlocks the True Potential of Neural Network-based Quantum Monte Carlo

TL;DR

This paper introduces a novel approach combining local pseudopotentials with neural network-based Quantum Monte Carlo, significantly enhancing computational efficiency and enabling accurate simulations of large, complex quantum systems previously beyond reach.

Contribution

The work presents a new method that integrates local pseudopotentials into NNQMC, improving scalability and accuracy for large many-body quantum systems.

Findings

Enables treatment of systems with up to 268 electrons.

Achieves better accuracy than all-electron NNQMC calculations.

Significantly reduces computational demands.

Abstract

Neural Network-based Quantum Monte Carlo (NNQMC), an emerging method for solving many-body quantum systems with high accuracy, has been limitedly applied to small systems due to demanding computation requirements. In this work, we introduce an approach based on local pseudopotentials to break through such limitation, significantly improving the computational efficiency and scalability of NNQMC. The incorporation of local pseudopotentials not only reduces the number of electrons treated in neural network but also achieves better accuracy than all electron NNQMC calculations for complex systems. This counterintuitive outcome is made possible by the distinctive characteristics inherent to NNQMC. Our approach enables the reliable treatment of large and challenging systems, such as iron-sulfur clusters with as many as 268 total electrons, which were previously beyond reach for NNQMC methods. Overall, our findings demonstrate that the synergy between NNQMC and local pseudopotentials substantially expands the scope of accurate ab initio calculations, pushing the frontiers of quantum chemistry and computational physics.