An Initial Design-based Deep Learning Procedure for the Optimization of High Dimensional ReaxFF Parameters

Atomistic level investigations are a significant part of today’s materials discovery research; however, most of the modeling methods that can describe chemical reactions are restricted to small molecular systems due to computational costs. The ReaxFF, an empirical interatomic potential, is capable of simulating reactions in larger molecular systems; however, the application of ReaxFF requires a significant preprocessing. One of the preprocessing steps is the optimization of functional parameters that are used to calculate interatomic interactions. This optimization process is complex due to high dimensionality. Here, we propose a deep learning (DL)-based procedure to be used in ReaxFF parameter optimization. The procedure is composed of three stages, which are: 1) data set creation; 2) DL model fitting and 3) local-minima detection. This DL procedure eliminates unfeasible regions in parameter space, which originate from the unphysical atomistic interactions, and constructs a more comprehensive understanding of a physically meaningful parameter landscape. The performance of the procedure will be evaluated by its application to molecular systems.