journal article·2024-05-28Open access

Efficient calculation of unbiased atomic forces in ab initio variational Monte Carlo

Kousuke Nakano, Michele Casula, Giacomo Tenti

Physical Review B· Volume 109· Issue 20

Abstract

quantum Monte Carlo (QMC) is a state-of-the-art numerical approach for evaluating accurate expectation values of many-body wave functions. However, one of the major drawbacks that still hinders widespread QMC applications is the lack of an affordable scheme to compute unbiased atomic forces. In this study, we propose an efficient method to obtain unbiased atomic forces and pressures in the variational Monte Carlo (VMC) framework with the Jastrow-correlated Slater determinant ansatz or the Jastrow antisymmetrized geminal power ansatz, exploiting the gauge-invariant and locality properties of their geminal representation. We demonstrate the effectiveness of our method for H 2 and Cl 2 molecules and for the cubic boron nitride crystal. Our framework has a better algorithmic scaling with the system size than the traditional finite-difference method and, in practical applications, is as efficient as single-point VMC calculations. Thus, it paves the way to study dynamical properties of materials, such as phonons, and is beneficial for pursuing more reliable machine-learning interatomic potentials based on unbiased VMC forces. Published by the American Physical Society 2024
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Research context

OpenAlex topics: Advanced Chemical Physics Studies · Quantum, superfluid, helium dynamics · Machine Learning in Materials Science

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

National Institute for Materials Science

Sorbonne Université

International School for Advanced Studies (SISSA)

Funding

National Institute for Materials Science · RIKEN · Ministry of Education, Culture, Sports, Science and Technology · Centre National de la Recherche Scientifique · Scuola Internazionale Superiore di Studi Avanzati · Grand Équipement National De Calcul Intensif · Horizon 2020

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