For training of the neural network interatomic potential (NNIP), DeePMD-kit (DP-kit) package (version 1.3.3) was employed. The Deep-Pot-Smooth Edition (DeepPot-SE) potential contained inside the DP-kit was chosen due to a smooth and continuously differentiable potential energy surface generation. The DeepPot-SE model learns a mapping between local environment of each atom within 8 Å cut-off to a per-atom energy, such that the sum of atomic energies corresponds to reference DFT energy. The gradients of the NNIP predicted energies are then used to compute the atomic forces. Both the reference energies and forces are included to evaluate the loss function which is minimized during training of a DeepPot-SE model. The training dataset comprised 11019 configurations at 510 °C and 10283 configurations at 700 °C containing LiF and BeF 2 of AIMD simulations with 91 atom supercells. During the training, the datasets were shuffled and were split in 80% and 20% for training and validation, respectively. Here, the smooth cutoff and hard cutoff radius of 2 Å and 8 Å were chosen. The embedding network and fitting network size were {25,50,100} and {240,240,240}, respectively. The tunable prefactors in loss function were chosen at 0.002, 1000, 1, and 1 for p start e , p start f , p limit e , and p limit f , respectively. These network parameters previously resulted in well-fitted potential energy surface for multicomponent molten salt systems. Here, the network was only trained on AIMD energies and forces. The trained network using DeepPot-SE yielded energy and force errors of 1.92 meV/atom and 22.1 meV/Å, respectively. The resulting errors are within the precision of DFT. The low energy and force training testing errors suggest a well-fitted potential energy surface.