INDEEDopt: a deep learning-based ReaxFF parameterization framework

Empirical interatomic potentials require optimization of force field parameters to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods. The optimization of the parameters is complex and requires the development of new techniques. 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, which finds the minimum discrepancy regions and eliminates unfeasible regions, and constructs a more comprehensive understanding of physically meaningful parameter space. We demonstrate the procedure here for the parameterization of a nickel-chromium binary force field and a tungsten-sulfide-carbon-oxygen-hydrogen quinary force field. We show that INDEEDopt produces improved accuracies in shorter development time compared to the conventional optimization method.

Automated summary

Empirical interatomic potentials usually require complex optimization of force field parameters to mimic quantum chemistry methods. This study introduces a new framework called INDEEDopt to accelerate and improve the quality of ReaxFF parameterization, demonstrating improved accuracy in shorter development time.