Download file (9.37 MB)
Download as zip (7.82 MB)

Collection

Citation

Description:

Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted manybody
method for solving the Schrödinger equation, known for its reliable predictions
of material and molecular properties. Furthermore, its excellent scalability
with system complexity and near-perfect utilization of computational power makes
FN-DMC ideally positioned to leverage new advances in computing to address increasingly
complex scientific problems. Even though the method is widely used as
a computational gold standard, reproducibility across the numerous FN-DMC code
implementations has yet to be demonstrated. This difficulty stems from the diverse
array of DMC algorithms and trial wave functions, compounded by the method’s
inherent stochastic nature. This study represents a community-wide effort to assess
the reproducibility of the method, affirming that: Yes, FN-DMC is reproducible
(when handled with care). Using the water-methane dimer as the canonical test
case, we compare results from eleven different FN-DMC codes and show that the
approximations to treat the non-locality of pseudopotentials are the primary source
of the discrepancies between them. In particular, we demonstrate that, for the same
choice of determinantal component in the trial wave function, reliable and reproducible
predictions can be achieved by employing the T-move (TM), the determinant
locality approximation (DLA), or the determinant T-move (DTM) schemes, while
the older locality approximation (LA) leads to considerable variability in results.
These findings demonstrate that, with appropriate choices of algorithmic details,
fixed-node DMC is reproducible across diverse community codes—highlighting the
maturity and robustness of the method as a tool for open and reliable computational
science.

Rights:

• Creative Commons BY Attribution 4.0 International
  

  Copyright 2025 Author(s). This article is distributed under a Creative Commons Attribution (CC BY) License.

Keyword: Quantum Monte Carlo, Diffusion Monte Carlo

Date published: 2025-09-14

Publisher: AIP Publishing

Journal:

• The Journal of Chemical Physics (ISSN: 00219606) vol. 163 issue. 10 104110

Funding:

• European Union Under the LERCO Project CZ.10.03.01/00/22_003/0000003
• Operational Program Just Transition
• U.S. Department of Energy
• Basic Energy Sciences
• Computational Materials Science Program
• Center for Predictive Simulation of Functional Materials
• U.S. National Science Foundation DMR-2316007
• U.S. National Science Foundation 1931258
• European Center of Excellence in Exascale Computing TREX
• HORIZON EUROPE European Research Council 952165
• JSPS Overseas Research Fellowships
• MEXT Leading Initiative for Excellent Young Researchers JPMXS0320220025
• European Union Under the Next Generation EU 20222FXZ33
• European Union Under the Next Generation EU P2022MC742
• Leverhulme Trust RPG-2020-038
• European Research Council 101071937
• Air Force Office of Scientific Research FA9550-18-1-0095
• Office of Science of the U.S. Department of Energy DE-AC05-00OR22725
• IT4Innovations National Supercomputing Center 90140
• HPC Facilities of the University of Luxembourg
• French Computational Resources at the CEA-TGCC Center Through the GENCI Allocation A0150906493
• National Energy Research Scientific Computing Center
• Dutch National Supercomputer Snellius
• Numerical Materials Simulator at National Institute for Materials Science
• Exascale Computing Project 17-SC-20-SC
• Engineering and Physical Sciences Research Council EP/T022159/1
• Engineering and Physical Sciences Research Council EP/P020259/1
• DiRAC Funding From the Science and Technology Facilities Council
• United Kingdom Car Parrinelloconsortium EP/ F036884/1

Manuscript type: Author's version (Accepted manuscript)

MDR DOI:

First published URL: https://doi.org/10.1063/5.0272974

Related item:

Other identifier(s):

Contact agent:

Updated at: 2025-12-02 08:30:07 +0900

Published on MDR: 2025-12-02 08:23:29 +0900