Machine Learning for Auto-tuning of Simulation Parameters in Car-Parrinello Molecular Dynamics

Simulating the dynamics of ions near polarizable nanoparticles (NPs) is challenging due to the need to solve the Poisson equation at every simulation timestep. Car-Parrinello Molecular dynamics (CPMD) simulations based on a dynamical optimization framework can bypass this obstacle by representing the polarization charge density as virtual dynamic variables, and evolving them in parallel with the physical dynamics of ions. Using these CPMD simulations of ions near polarizable NPs, we demonstrate the computational gains accessible by integrating machine learning (ML) for parameter prediction in CPMD simulations. An artificial neural network based regression model was integrated with CPMD and it predicted the optimal simulation timestep and critical parameters characterizing the virtual system on-the-fly with 94.3% accuracy. The ML-enhanced, hybrid OpenMP/MPI parallelized, CPMD simulations generated stable and accurate dynamics of thousands of ions in the presence of polarizable NPs for over 10 million steps (over 30 ns) with walltime reducing from thousands of hours to tens of hours yielding a maximum speedup of ~600.