Download file (1.13 MB)
Download as zip (972 KB)

Collection

Citation

Description:

Variational Monte Carlo (VMC) can be used to train accurate machine learning interatomic potentials (MLIPs), enabling molecular dynamics (MD) simulations of complex materials on time scales and for system sizes previously unattainable. VMC training sets are often based on partially optimized wave functions (WFs) to circumvent expensive energy optimizations of the whole set of WF parameters. However, frozen variational parameters lead to VMC forces and pressures not consistent with the underlying potential energy surface, a bias called the self-consistency error (SCE). Here, we demonstrate how the SCE can spoil the accuracy of MLIPs trained on these data, taking high-pressure hydrogen as test case. We then apply a recently introduced SCE correction [ Phys. Rev. B 109, 205151 (2024)] to generate unbiased VMC training sets based on a Jastrow-correlated single determinant WF with frozen Kohn-Sham orbitals. The MLIPs generated within this framework are significantly improved and can approach in quality those trained on datasets built with fully optimized WFs. Our conclusions are further supported by MD simulations, which show how MLIPs trained on SCE-corrected datasets systematically yield more reliable physical observables. Our framework opens the possibility of constructing extended high-quality training sets with VMC.

Rights:

• http://arxiv.org/licenses/nonexclusive-distrib/1.0/
  

Keyword: Quantum Monte Carlo, Variational Monte Carlo, Atomic forces

Date published: 2025-10-14

Publisher: American Chemical Society (ACS)

Journal:

• Journal of Chemical Theory and Computation (ISSN: 15499618) vol. 21 issue. 19 p. 9335-9346

Funding:

• European High Performance Computing Joint Undertaking EHPC-EXT-2024E01-064
• Ministry of Education, Culture, Sports, Science and Technology JPMXS0320220025
• Murata Science and Education Foundation M24AN006
• Japan Science and Technology Agency (JST) JPMJPR24J9
• European High Performance Computing Joint Undertaking Hpc AlliaNce for Applications and supercoMputing Innovation

Manuscript type: Author's version (Accepted manuscript)

MDR DOI:

First published URL: https://doi.org/10.1021/acs.jctc.5c00715

Related item:

Other identifier(s):

Contact agent:

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

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