dl-data_nicr.csv

ReaxFF is an empirical interatomic potential capable of simulating reactions in complex chemical processes and thus determine the dynamical evolution of the molecular systems. A drawback of this method is the necessity of a significant development stage to adapt it to a chemical system of interest. One of these stages is the optimization of force field parameters that are used to tune1interatomic interactions to mimic ones obtained by quantum chemistry-based methods. The optimization of these parameters is a very complex high dimensional problem. Here, we propose an INitial-DEsign Enhanced Deep learning-based OPTimization (INDEEDopt) framework to accelerate and improve the quality of the ReaxFF parameterization. The procedure starts with a Latin Hypercube Design (LHD) algorithm that is used to explore the parameter landscape extensively. The LHD passes the information about explored regions to a deep learning model for training. The deep learning model finds the minimum discrepancy regions and eliminates unfeasible regions, which originate from the unphysical atomistic interactions, and constructs a more comprehensive understanding of a physically meaningful parameter space. We demonstrate the procedure here for the parameterization of a nickel-chromium binary force field and a tungsten- sulfide-carbon-hydrogen quaternary force field. We demonstrate that INDEEDopt produces improved accuracies in shorter development time compared to the conventional linear optimization method.