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

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