Abstract
Fixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely trusted many-body 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 make 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, the determinant locality approximation, or the determinant T-move schemes, while the older locality approximation 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.
| Original language | English |
|---|---|
| Article number | 104110 |
| Journal | Journal of Chemical Physics |
| Volume | 163 |
| Issue number | 10 |
| DOIs | |
| State | Published - Sep 14 2025 |
Funding
M.B. acknowledges the computational resources from the HPC facilities of the University of Luxembourg (see hpc.uni.lu). M.D. acknowledges the financial support from the European Union under the LERCO Project No. CZ.10.03.01/00/22_003/0000003, via the Operational Program Just Transition, and computational resources from the IT4Innovations National Supercomputing Center (e-INFRA CZ, ID: 90140). M.C. acknowledges the access to French computational resources at the CEA-TGCC center under the GENCI Allocation No. A0150906493. J.T.K., P.R.C.K., Y.L., and L.M. were supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials. Part of the work of L.M. has also been supported by the U.S. National Science Foundation Grant No. DMR-2316007 and employed resources at NERSC at the early stages of this project. M.C., E.S., R.S., and C.F. acknowledge the partial support by the European Center of Excellence in Exascale Computing, TREX—Targeting Real Chemical Accuracy at the Exascale. This project has received the funding in part from the European Union’s Horizon 2020—Research and Innovation program—under Grant Agreement No. 952165. E.S., R.S., and C.F. performed the calculations on the Dutch national supercomputer Snellius with the support of SURF Cooperative. K.N. acknowledges the financial support from the JSPS Overseas Research Fellowships and from MEXT Leading Initiative for Excellent Young Researchers (Grant No. JPMXS0320220025) and computational resources from the Numerical Materials Simulator at the National Institute for Materials Science (NIMS). L.K.W. and W.A.W. were supported by the U.S. National Science Foundation via Award No. 1931258. Y.S.A., A.Z., and D.A. acknowledge the support from the European Union under the Next Generation EU (Project Nos. 20222FXZ33 and P2022MC742) and from Leverhulme Grant No. RPG-2020-038. A.M. and B.X.S. acknowledge the support from the European Union under the “n-AQUA” European Research Council project (Grant No. 101071937). The portion of the work done by T.A.A. and C.J.U. received the initial support under AFOSR (Grant No. FA9550-18-1-0095) and was completed under the Exascale Computing Project (No. 17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. M.B. acknowledges the computational resources from the HPC facilities of the University of Luxembourg140 (see hpc.uni.lu). M.D. acknowledges the financial support from the European Union under the LERCO Project No. CZ.10.03.01/00/22_003/0000003, via the Operational Program Just Transition, and computational resources from the IT4Innovations National Supercomputing Center (e-INFRA CZ, ID: 90140). M.C. acknowledges the access to French computational resources at the CEA-TGCC center under the GENCI Allocation No. A0150906493. J.T.K., P.R.C.K., Y.L., and L.M. were supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials. Part of the work of L.M. has also been supported by the U.S. National Science Foundation Grant No. DMR-2316007 and employed resources at NERSC at the early stages of this project. M.C., E.S., R.S., and C.F. acknowledge the partial support by the European Center of Excellence in Exascale Computing, TREX—Targeting Real Chemical Accuracy at the Exascale. This project has received the funding in part from the European Union’s Horizon 2020—Research and Innovation program—under Grant Agreement No. 952165. E.S., R.S., and C.F. performed the calculations on the Dutch national supercomputer Snellius with the support of SURF Cooperative. K.N. acknowledges the financial support from the JSPS Overseas Research Fellowships and from MEXT Leading Initiative for Excellent Young Researchers (Grant No. JPMXS0320220025) and computational resources from the Numerical Materials Simulator at the National Institute for Materials Science (NIMS). L.K.W. and W.A.W. were supported by the U.S. National Science Foundation via Award No. 1931258. Y.S.A., A.Z., and D.A. acknowledge the support from the European Union under the Next Generation EU (Project Nos. 20222FXZ33 and P2022MC742) and from Leverhulme Grant No. RPG-2020-038. A.M. and B.X.S. acknowledge the support from the European Union under the “n-AQUA” European Research Council project (Grant No. 101071937). The portion of the work done by T.A.A. and C.J.U. received the initial support under AFOSR (Grant No. FA9550-18-1-0095) and was completed under the Exascale Computing Project (No. 17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. Calculations were also performed using the Cambridge Service for Data Driven Discovery (CSD3) operated by the University of Cambridge Research Computing Service ( www.csd3.cam.ac.uk ), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital Grant Nos. EP/T022159/1 and EP/P020259/1), and DiRAC funding from the Science and Technology Facilities Council ( www.dirac.ac.uk ). This work also used the ARCHER UK National Supercomputing Service ( https://www.archer2.ac.uk ) and the United Kingdom Car Parrinello (UKCP) consortium (Grant No. EP/F036884/1). F.D.P., B.X.S., and A.M. acknowledge EuroHPC Joint Undertaking for awarding the Project ID EHPC-REG-2024R02-130 access to Leonardo at CINECA, Italy.