We presented a new software called JAX-ReaxFF that enables extremely fast optimization of Reax force field parameters by leveraging recent advances in machine learning. JAX-ReaxFF uses several innovative techniques for high performance on architectures with GPUs. Clustering similar geometries together to maximize the SIMD parallelism while limiting the padding for alignment yields high parallelism, especially for single step evaluations. As it is described in Algorithm 3, by using single step energy evaluation based approximations to the error function and gradient information about the search space, we are able to decrease the convergence time significantly with the help of GPU acceleration. We have empirically showed that even though the local optimizer is not fully aware of the geometry optimization, the overall algorithm converges with changes in parameter space becoming minimal as the algorithm progresses.