The process of training and evaluating DPs is illustrated in Figure . Each DP was fitted to training sets consisting of DFT calculations on the periodic cells of graphanol. Each DP contains an embedding and a fitting neural network (NN) that uses atomic coordinates as input and gives total energies and forces as output. The total energy of the system was computed as the sum of individual atomic energies. The energy of an atom i was calculated based on the number of neighboring atoms within a cutoff radius. We set a cutoff radius of 6 Å with a smoothing cutoff of 2 Å for the atomic coordinates, which were used to construct descriptors. The fitting NN used these descriptors as input. The training dataset was distributed among several batches to train each DP.