We focus on the ReaxFF method, which is one of the most impactful reactive force fields, if not the most impactful one . If there is an existing Reax force field for a similar purpose, it could be sufficient to fine-tune that for the new target application. When a new force field needs to be developed from scratch, multiple passes over the training data may be necessary, i.e., based on the quality of the resulting force field, the training data itself may need to go through revisions. In both scenarios, the training speed is crucial. As such, development of high-quality and fast optimization methods for ReaxFF has been an active research topic, first starting with the sequential one-parameter parabolic interpolation method (SOPPI) by van Duin , and then continuing with various global optimization methods such as genetic algorithms (GAs) , simulated annealing (SA) , evolutionary algorithms (EAs) , particle swarm optimization (PSO) . More recently, machine learning based search methods have been employed for this purpose .