The earliest ReaxFF optimization tool is the sequential parabolic parameter interpolation method (SOPPI) . SOPPI uses a one-parameter-at-a-time approach where consecutive single parameter searches are performed until a certain convergence criteria is met. The algorithm is simple, but as the number of parameters increases, the number of one-parameter optimization steps needed for convergence increases drastically. Also, the success of this method is very dependent on the initial guess and the order of the parameters to be optimized. Due to these drawbacks of SOPPI, various global methods such as genetic algorithms (GAs) , simulated annealing (SA) , evolutionary algorithms (EAs) , particle swarm optimization (PSO) and machine learning based search methods have been investigated for ReaxFF optimization. For an explanation and evaluation of the most promising of these methods, we refer readers to the prior work by Shchygol et al. . These methods have been proven to be successful for ReaxFF optimization. However, due to the absence of any gradient information, these global search methods require a high number of potential energy evaluations, as such they can be very costly.