Hardware Setup All the CPU experiments reported here were conducted on a computer with two Intel(R) Xeon(R) CPU E5-2680 v4 @ 2.40GHz (2x14 cores) CPU processors, 128 GB 2133 MHz DDR4 Ram. The GPU experiments were conducted on a computer with Intel(R) Core(TM) i7-9700K CPU @ 3.60GHz, 16 GB 3000 MHz DDR4 Ram and single 1080-TI GPU card (11 GB GDDR5X memory). For the baseline methods, OGOLEM version 1.0with sPuReMD backend is used. The proposed method is implemented in Python 3. MCFF optimizer utilizes the simulated annealing approach to slowly modify the parameters and act based on the change in the error value. The remaining two approaches are population based and are inspired by the basic principles of biological evolution. In GA, a population of candidate solutions for a given optimization problem is evolved towards better solutions. Typically, evolution happens through random mutations and cross-overs between selected candidate solutions. In CMA-ES, new solutions are sampled from a multivariate normal distribution. The pairwise dependencies between the parameters are captured by the covariance matrix and as the search progresses, CMA-ES updates the covariance matrix. All three approaches use ReaxFF model as a black box and find the direction solely from the function evaluations. Shchygol et al. has compared these methods on different training tasks without focusing on tuning them and repeated the experiments multiple times with different starting conditions. Since they have provided an important test bench to compare different optimizers for ReaxFF, we follow the same approach to evaluate JAX-ReaxFF. Since the exact software and hardware from are not accessible, execution times for the baseline methods are approximated on the hardware described before.We have calculated the time per true error evaluation for each training task using OGOLEM with sPuReMD backend and multiplied this by the total number of error evaluations presented in . This approximation is a lower bound for CMA-ES and MCFF since they have a lower level of parallelism unlike genetic algorithm where each evaluation is independent from each other.