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This research introduces practical variance-reduction techniques specifically tailored for variational Monte Carlo calculations of atomic forces and pressure in electronic systems. By modifying Pulay-type contributions and deriving compact estimators for Hellmann-Feynman forces, the method significantly improves stability for large-scale simulations involving neural quantum states, such as high-pressure metallic hydrogen. These advancements directly enhance the efficiency and accuracy of quantum many-body computations without relying on incidental AI applications.
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Variance reduction for forces and pressure in variational Monte Carlo

arXiv2 min read

arXiv:2603.14521v2 Announce Type: replace-cross Abstract: We present simple and practical strategies to reduce the variance of Monte Carlo estimators. Our focus is on variational Monte Carlo calculations of atomic forces and pressure in electronic systems, although we show that the underlying ideas apply more broadly to other observables, like pair-correlation and angular-distribution functions, and other methods, including molecular dynamics. For Pulay-type contributions, we show that a minor modification based on the Metropolis acceptance ratio softens the power-law divergence of the variance to a logarithmic one, and that inexpensive regularizations can further suppress outliers at the price of a controlled small bias. For Hellmann-Feynman forces, we derive compact variance-reduced estimators for periodic systems that are straightforward to implement in standard Monte Carlo codes. The approach is illustrated for high-pressure metallic hydrogen with more than a hundred atoms described by neural quantum states, including an application to molecular dynamics driven by the improved forces.

Abstract:We present simple and practical strategies to reduce the variance of Monte Carlo estimators. Our focus is on variational Monte Carlo calculations of atomic forces and pressure in electronic systems, although we show that the underlying ideas apply more broadly to other observables, like pair-correlation and angular-distribution functions, and other methods, including molecular dynamics. For Pulay-type contributions, we show that a minor modification based on the Metropolis acceptance ratio softens the power-law divergence of the variance to a logarithmic one, and that inexpensive regularizations can further suppress outliers at the price of a controlled small bias. For Hellmann-Feynman forces, we derive compact variance-reduced estimators for periodic systems that are straightforward to implement in standard Monte Carlo codes. The approach is illustrated for high-pressure metallic hydrogen with more than a hundred atoms described by neural quantum states, including an application to molecular dynamics driven by the improved forces.

Strongly Correlated Electrons (cond-mat.str-el); Quantum Physics (quant-ph)

arXiv:2603.14521 [cond-mat.str-el]

(or arXiv:2603.14521v2 [cond-mat.str-el] for this version)

https://doi.org/10.48550/arXiv.2603.14521 Focus to learn more arXiv-issued DOI via DataCite

J. Chem. Phys. 165, 134106 (2026)

https://doi.org/10.1063/5.0336683 Focus to learn more DOI(s) linking to related resources

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Full text under the publisher's open licence · 259 words

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