Training potential on well-optimized structures generally limits its comprehension of nonequilibrium behavior, impeding its ability to precisely predict non-zero forces as atoms deviate from their ideal positions. In order to capture non-zero forces, the training dataset was gradually augmented by randomly selection of structures from the initial dataset and were subjected to random atomic position displacements, each with an amplitude of 0.05 Å, in subsequent training cycles. Additional structures were randomly generated and optimized using DFT to expand the dataset (Data Augmentation in Fig. ). This was necessary because the initial generation of the ANN potential failed in geometry optimization, resulting in widely dispersed or collapsed structures. After improving the ANN potential to handle reasonable geometry optimization without such issues, additional structures were generated and used as initial guesses to explore low-energy regions of the energy landscape. This exploration was carried out using the minima hopping global geometry optimization method (MHM) and the enhanced ANN potential. The resulting structures were filtered based on the fingerprint method discussed in the previous paragraph. This filtering ensured the diversity of the training data. Then, single point (SP) calculations with DFT was done for the selected structures to get the energy (E) and forces (F). In this way, we generated a dataset consisting of 26, 731 structures, from which 14, 664 structures were included in training the final ANN potential due to the energy filtering that will be discussed in section .