gives the gradients for the force matching items in the objective func- ) result in extremely high values, ∼ 10 17 , while the other gradients are much lower. These high gradients stop the local optimizers from doing any progress as seen in Fig. . Since bond order parameters form the core of the dynamic bond concept in ReaxFF and therefore affect all types of bonded interactions, highly sensitive functional form for the bond order calculation , using relatively large parameter ranges and initial guesses being off of their ideal values are likely responsible for this behaviour. While not ideal, we excluded the bond order parameters from optimization and fixed their values to the literature ones. Among 87 parameters, 18 parameters are removed and the optimization is performed again with the remaining 69 parameters. As shown in Fig. , this improves results drastically, and JAX-ReaxFF can attain better scores than the baseline methods in significantly shorter time, but it should be noted that the comparison cases include all 87 parameters. This situation shows that gradient-based optimization is prone to failures for parameters with a large influence on the objective function like the bond order parameters. However, in practice training items for bond order optimization are easy to construct and their optimization can be performed independently prior to the actual optimization task. For this reason, we do not see this issue to be a major limitation for JAX-ReaxFF's practical use.