In JAX-ReaxFF, as mentioned above, two different gradient based optimization algorithms are available, L-BFGS-B and SLSQP. For both L-BFGS-B and SLSQP, the maximum number iterations is set to 100. This iteration number is for the step 3 of Algorithm 3. For L-BFGS-B, the maximum number of iterations for the line search is set to 20 and the maximum number of variable matrix corrections to approximate the Hessian matrix is set to 20. For the rest of the control parameters, the default values from the SciPy library are used. The iteration count for the main optimization loop of Algorithm 3 is set to 20 where the local error minimization and the geometry optimization steps are iteratively repeated this many times. Therefore, for all of the experiments for Jax-ReaxFF, the true error calculation with geometry optimization is done 20 times since the local error minimization only uses single step calculations.