FMM-AutoOpt: A Python Tool for Automated GROMACS FMM Parameter Optimization

Abstract The fast multipole method (FMM) for computing Coulombic forces in molecular dynamics (MD) simulations offers benefits for a variety of systems, including nonperiodic configurations such as molecules in vacuum and large-scale systems where many other approaches encounter limitations. Because FMM parameters govern both simulation accuracy and computational performance, and because the optimal solution is highly system-dependent, we have developed FMM-AutoOpt for the automated benchmarking of FMM parameters with GROMACS. Relying solely on standard Python libraries and GROMACS FMM (Kohnke, B.; et al.J. Chem. Theory Comput.2020, 16, 6938–6949.), the tool is versatile and flexible, facilitating rapid parameter screening to ensure optimal performance and high simulation accuracy.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2026-09-04
DOI
https://doi.org/10.1021/acs.jcim.6c01211
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
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article

FMM-AutoOpt: A Python Tool for Automated GROMACS FMM Parameter Optimization

Louise Persson, Erik G. Marklund
Journal of Chemical Information and Modeling
Protein Structure and Dynamics
article

FMM-AutoOpt: A Python Tool for Automated GROMACS FMM Parameter Optimization

Louise Persson, Erik G. Marklund
article en

Abstract

Abstract The fast multipole method (FMM) for computing Coulombic forces in molecular dynamics (MD) simulations offers benefits for a variety of systems, including nonperiodic configurations such as molecules in vacuum and large-scale systems where many other approaches encounter limitations. Because FMM parameters govern both simulation accuracy and computational performance, and because the optimal solution is highly system-dependent, we have developed FMM-AutoOpt for the automated benchmarking of FMM parameters with GROMACS. Relying solely on standard Python libraries and GROMACS FMM (Kohnke, B.; et al.J. Chem. Theory Comput.2020, 16, 6938–6949.), the tool is versatile and flexible, facilitating rapid parameter screening to ensure optimal performance and high simulation accuracy.

Journal of Chemical Information and Modeling
Uppsala University (SE)
Openalex Percentile: Top 17%
Protein Structure and Dynamics
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