Work

Reducing the Cost of Energy Differences in Variational Monte Carlo with Spotlight Sampling

Year: 2025

Type: article

Abstract: We investigate an approximate sampling scheme that can significantly reduce the cost scaling of variational Monte Carlo when it is employed to predict the energy differences associated with local chemical changes. Inspired by side-chaining and embedding methods, this spotlight sampling approach adopts an approximate fragmented Hamiltonian and correlated sampling to reduce cost scaling to the point that it is essentially linear with system size, with the potential to go sublinear if certain conditions are met. In tests on bond stretching energies in alcohols, hydrogen dimer chains, and molecules with various degrees of π-system delocalization, we observe the anticipated linear scaling and an explicit cost crossover with standard variational Monte Carlo.

Source: Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.5c01232

Authors: Sonja Bumann, Eric Neuscamman

Institutions: Lawrence Berkeley National Laboratory, University of California, Berkeley

Language: English

Topic: Advanced Chemical Physics Studies

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: U.S. Department of Energy, Office of Science, Basic Energy Sciences, Lawrence Berkeley National Laboratory