Machine Learning for Parameter Auto-tuning in Molecular Dynamics Simulations: Efficient Dynamics of Ions near Polarizable Nanoparticles

TL;DR

This paper introduces a machine learning-enhanced method for auto-tuning parameters in molecular dynamics simulations of ions near polarizable nanoparticles, significantly improving efficiency and stability.

Contribution

The authors developed an ML-based auto-tuning framework integrated with MD simulations, enabling accurate and stable ion dynamics with reduced computational time.

Findings

ML predicted optimal parameters with 94.3% success

Simulations achieved 10 million steps with improved stability

Computational time reduced from thousands of hours to tens of hours

Abstract

Simulating the dynamics of ions near polarizable nanoparticles (NPs) using coarse-grained models is extremely challenging due to the need to solve the Poisson equation at every simulation timestep. Recently, a molecular dynamics (MD) method based on a dynamical optimization framework bypassed this obstacle by representing the polarization charge density as virtual dynamic variables, and evolving them in parallel with the physical dynamics of ions. We highlight the computational gains accessible with the integration of machine learning (ML) methods for parameter prediction in MD simulations by demonstrating how they were realized in MD simulations of ions near polarizable NPs. An artificial neural network based regression model was integrated with MD simulation and predicted the optimal simulation timestep and optimization parameters characterizing the virtual system with $94.3\%$ success. The ML-enabled auto-tuning of parameters generated accurate dynamics of ions for $\approx 10$ million steps while improving the stability of the simulation by over an order of magnitude. The integration of ML-enhanced framework with hybrid OpenMP/MPI parallelization techniques reduced the computational time of simulating systems with thousands of ions and induced charges from thousands of hours to tens of hours, yielding a maximum speedup of $\approx 3$ from ML-only acceleration and a maximum speedup of $\approx 600$ from the combination of ML and parallel computing methods. Extraction of ionic structure in concentrated electrolytes near oil-water emulsions demonstrates the success of the method. The approach can be generalized to select optimal parameters in other MD applications and energy minimization problems.