Overcoming the Memory Bottleneck in Auxiliary Field Quantum Monte Carlo Simulations with Interpolative Separable Density Fitting

2018/10/31 by Fionn D. Malone, Fionn D Malone, Shuai Zhang +1 · 56 citations
Mathematics · Physics and Astronomy · #Advanced Chemical Physics Studies #Algorithm #Bottleneck #Computational science #Computer science #Field (mathematics) #Mathematics #Monte Carlo method #Physics #Physics of Superconductivity and Magnetism #Quantum and electron transport phenomena #Statistical physics #Statistics #physics.chem-ph #physics.comp-ph

paper · pdf · doi:10.1021/acs.jctc.8b00944

published in Journal of Chemical Theory and Computation 15(1), 256-264 (American Chemical Society) · Accepted Version

openalex publication_date 2018/12/19 · arxiv created 2018/12/21 · arxiv updated 2018/12/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

We investigate the use of interpolative separable density fitting (ISDF) as a means to reduce the memory bottleneck in auxiliary field quantum Monte Carlo (AFQMC) simulations of real materials in Gaussian basis sets. We find that ISDF can reduce the memory scaling of AFQMC simulations from [Formula: see text] to [Formula: see text]. We test these developments by computing the structural properties of carbon in the diamond phase, comparing to results from existing computational methods and experiment.

Citations

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