Performance Tuning of Polarizable Gaussian Multipole Model in Molecular Dynamics Simulations.

Molecular dynamics (MD) simulations are essential for understanding molecular phenomena at the atomic level, with their accuracy largely dependent on both the employed force field and sampling. Polarizable force fields, which incorporate atomic polarization effects, represent a significant advancement in simulation technology. The polarizable Gaussian multipole (pGM) model has been noted for its accurate reproduction of ab initio electrostatic interactions. In this study, we document our effort to enhance the computational efficiency and scalability of the pGM simulations within the AMBER framework using MPI (message passing interface). Performance evaluations reveal that our MPI-based pGM model significantly reduces runtime and scales effectively while maintaining computational accuracy. Additionally, we investigated the stability and reliability of the MPI implementation under the simulation ensemble. Optimal Ewald and induction parameters for the pGM model are also explored, and its statistical properties are assessed under various simulation ensembles. Our findings demonstrate that the MPI-implementation maintains enhanced stability and robustness during extended simulation times. We further evaluated the model performance under both (constant number, volume, and temperature) and (constant number, pressure, and temperature) ensembles and assessed the effects of varying timesteps and convergence tolerance on induced dipole calculations. The lessons learned from these exercises are expected to help the users to make informed decisions on simulation setup. The improved performance under these ensembles enables the study of larger molecular systems, thereby expanding the applicability of the pGM model in detailed MD simulations.