Hybrid Molecular Dynamics-Machine Learning Approach for Efficient Modeling of Particle Growth in Non-Thermal Plasma

The growth of particles in non-thermal plasma is a fascinating yet challenging problem to model accurately due to its non-equilibrium nature. Although it is feasible to model specific reactions occurring during particles' precursor growth, extending this approach becomes rapidly unfeasible. In this study, we propose a hybrid molecular dynamics-machine learning approach that significantly reduces computational requirements. To demonstrate the effectiveness of our approach, we examined the collisions between silane molecules using classical molecular dynamics simulations. By decoupling the internal energy from the collision speed, we conducted simulations that allow for rapid computation of results and uncertainties for any translational energy distribution. Using these simulations, we determined the probability of different reactions at various temperatures, information that was then used to train machine learning models that investigate the best inference of missing data from sampled conditions. The results indicate that machine learning can predict missing interactions, but caution must be exercised in the selection of molecular dynamics-generated data to achieve optimal accuracy and computational time reduction.