where pe and pf are the weight for the energy and force terms, respectively. N represents the number of atoms in the structure. Similar to a classical neural network, the DP scheme trains the model by computing the gradient of the loss function using the back-propagation algorithm . The NNP is trained for 1.0 × 10 6 iterations with an exponentially decaying learning rate from 1.0 × 10 -3 to 5.0× 10 -8 . , 3000, and 4000 K using the ReaxFF forcefield . One thousand configurations are randomly selected from the above trajectories to build the initial dataset, and high-level DFT calculations are further conducted to obtain accurate energies and forces. DFT calculations are performed using the CP2K package .