where ∆E and ∆F i are the root mean square (RMS) errors of the energy of the system and the force F on atom i, N is the number of atoms, and p ε and p f are the adjustable pre-factors. As the training proceeded, p ε began at 0.02 and ended at 8, and p f changed from 1000 to 1. The initial learning rate was 0.0001 with a decay rate of 0.95 for 5,000 total decay steps. The loss function optimization was done using the Adam stochastic gradient descent method. The batch-size was 5 and the training process proceeded for 1,000,000 steps. The training data consisted of energies and forces from AIMD simulations.