The initial guess is an important factor which could change the results drastically. It is especially important for gradient based optimizations because these methods cannot move through the space freely as they need to follow the direction of the gradients. To show the capabilities of JAX-ReaxFF, we experimented with all three initialization methods from Shchygol et al. , namely random, educated and literature based initial guesses. For random initial guesses, initial values are sampled from a uniform distribution based on the given parameter ranges. To produce educated guesses, prior information from the previous related force fields is used as it is described further in . For the literature based initial guesses, the force fields developed previously using the same training data are used. To give more reliable results, each initialization method is repeated ten times. For the educated and literature based initial guesses, small amount of uniform random noise is added to the parameters without violating the range restrictions. For each parameter p, the noise value is sampled from [ -1k 10 , 1k 10 ] where k is the length of the given range for parameter p. For the random initial guesses, uniform sampling is done ten times to produce the guesses.