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Peer-reviewedCC BYJournal of chemical information and modeling

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

  • Louise J Persson1
  • Erik G Marklund1
  1. 1Department of Chemistry for Life Sciences, Uppsala University, SE-75123Uppsala, Sweden.
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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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Cite

BibTeXRIS
BibTeX
@article{persson2026fmmautoopt,
  title = {{FMM-AutoOpt: A Python Tool for Automated GROMACS FMM Parameter Optimization.}},
  author = {Louise J Persson and Erik G Marklund},
  journal = {Journal of chemical information and modeling},
  year = {2026},
  month = sep,
  doi = {10.1021/acs.jcim.6c01211},
  url = {https://europepmc.org/article/MED/42906030},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - FMM-AutoOpt: A Python Tool for Automated GROMACS FMM Parameter Optimization.
AU  - Louise J Persson
AU  - Erik G Marklund
PY  - 2026
DA  - 2026/09/01
JO  - Journal of chemical information and modeling
DO  - 10.1021/acs.jcim.6c01211
UR  - https://europepmc.org/article/MED/42906030
AB  - 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.
SN  - 1549-9596
SN  - 1549-960X
ER  -