Assessing the impact of nodal surface optimization in fixed-node diffusion Monte Carlo on non-covalent interactions

TL;DR

This study evaluates how optimizing the nodal surface in fixed-node diffusion Monte Carlo improves accuracy for noncovalent interactions, especially hydrogen bonds, by using a novel antisymmetrized geminal power approach.

Contribution

It introduces a nodal surface optimization method with natural orbitals that enhances DMC accuracy for hydrogen-bonded systems compared to standard approaches.

Findings

Improved agreement with CCSD(T) for hydrogen-bonded interactions.

Negligible effect on dispersion-dominated systems.

Provides a practical route to reduce discrepancies in hydrogen-bonded NCIs.

Abstract

Diffusion quantum Monte Carlo (DMC) and coupled cluster theory [CCSD(T)] are widely-employed benchmark methods for noncovalent interactions (NCIs). However, recent studies have reported notable discrepancies across several hydrogen-bonded and dispersion-dominated systems, raising questions on the accuracy of the approximations underlying each approach. In DMC, the dominant error is expected to stem from the fixed-node approximation, where the nodal surface is typically taken from a single Slater determinant derived from a density functional theory or Hartree-Fock calculation. In this work, we assess the impact of nodal surface optimization on DMC predictions for 12 compounds spanning diverse NCIs, using a recently proposed antisymmetrized geminal power ansatz with natural orbitals. We find improved agreement with CCSD(T) for hydrogen-bonded systems, while having negligible effect for dispersion-dominated systems. These results provide a practical and computationally efficient route to resolving discrepancies in hydrogen-bonded interactions, while offering insight into the remaining differences in dispersion-dominated systems.