With the emergence of optimized tools for machine learning to calculate gradients of complex functions automatically, a method called Intelligent-ReaxFF has been proposed to leverage these tools to train Reax force fields . They use the TensorFlow library to calculate the gradients and optimize a force field. However, the method does not have the flexibility of the previously mentioned methods in terms of the training data. The force field only can be trained to match the ReaxFF energies to the absolute energies in the reference data; relative energies, charges or forces cannot be used in the training, essentially limiting its usability. Also since it does not filter out the unnecessary 2-body, 3-body and 4-body interactions before the optimization step, it is significantly slower than JAX-ReaxFF.