Coupling LAMMPS with LPC3D for Mesoscopic Simulations of Electrolytes in Porous Carbons: A Python-MPI Workflow for CPU and GPU Supercomputers
Abstract
Mesoscopic simulations are useful for predicting properties of supercapacitors thanks to their ability to bridge the gap between atomistic detail and device-scale behavior, enabling the study of ion transport, charge storage, and pore-scale phenomena. In particular, mesoscopic lattice-gas simulations can access electrode length scales that remain outside the reach of fully atomistic molecular dynamics (MD) simulations due to the high computational costs of the latter. However, mesoscopic simulations require pore-resolved microscopic data that typically depend on pore size and electrolyte composition. This work presents a practical coupling between LAMMPS, a classical MD code with a focus on materials modeling, and the lattice-gas code LPC3D, in which LPC3D acts as the driver of an ensemble of independent LAMMPS simulations, one for each pore size. The coupling is implemented in Python by combining mpi4py based communicator splitting with the LAMMPS Python interface, enabling concurrent MD executions within a single MPI allocation. In order to parameterize pore-dependent fields for large-scale simulations of heterogeneous porous electrodes, post-processing procedures extract pore-resolved equilibrium number densities from MD trajectories and transfer these quantities to LPC3D. We describe deployment on high performance computing (HPC) systems in CPU-only mode (IntelMPI) and in GPU-accelerated mode (IntelMPI with Kokkos and NVIDIA backends), and we report the strong and weak scaling behaviors of the coupled workflow on multi node architectures.
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BibTeX
@article{lahrar2026coupling,
title = {{Coupling LAMMPS with LPC3D for Mesoscopic Simulations of Electrolytes in Porous Carbons: A Python-MPI Workflow for CPU and GPU Supercomputers}},
author = {El Hassane Lahrar and Mohamed Houssein Mohamed and Mathieu Salanne and Guillaume Jeanmairet and Caspar van Leeuwen and Céline Merlet},
journal = {Procedia Computer Science},
year = {2026},
month = sep,
doi = {10.1016/j.procs.2026.08.019},
url = {https://hal.science/hal-05757076v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Coupling LAMMPS with LPC3D for Mesoscopic Simulations of Electrolytes in Porous Carbons: A Python-MPI Workflow for CPU and GPU Supercomputers
AU - El Hassane Lahrar
AU - Mohamed Houssein Mohamed
AU - Mathieu Salanne
AU - Guillaume Jeanmairet
AU - Caspar van Leeuwen
AU - Céline Merlet
PY - 2026
DA - 2026/09/07
JO - Procedia Computer Science
DO - 10.1016/j.procs.2026.08.019
UR - https://hal.science/hal-05757076v1
AB - Mesoscopic simulations are useful for predicting properties of supercapacitors thanks to their ability to bridge the gap between atomistic detail and device-scale behavior, enabling the study of ion transport, charge storage, and pore-scale phenomena. In particular, mesoscopic lattice-gas simulations can access electrode length scales that remain outside the reach of fully atomistic molecular dynamics (MD) simulations due to the high computational costs of the latter. However, mesoscopic simulations require pore-resolved microscopic data that typically depend on pore size and electrolyte composition. This work presents a practical coupling between LAMMPS, a classical MD code with a focus on materials modeling, and the lattice-gas code LPC3D, in which LPC3D acts as the driver of an ensemble of independent LAMMPS simulations, one for each pore size. The coupling is implemented in Python by combining mpi4py based communicator splitting with the LAMMPS Python interface, enabling concurrent MD executions within a single MPI allocation. In order to parameterize pore-dependent fields for large-scale simulations of heterogeneous porous electrodes, post-processing procedures extract pore-resolved equilibrium number densities from MD trajectories and transfer these quantities to LPC3D. We describe deployment on high performance computing (HPC) systems in CPU-only mode (IntelMPI) and in GPU-accelerated mode (IntelMPI with Kokkos and NVIDIA backends), and we report the strong and weak scaling behaviors of the coupled workflow on multi node architectures.
SN - 1877-0509
SN - 1877-0509
ER -