To ensure representativity and transferability, the dataset was constructed through two stages. First, the data for Cu and Cu2O were built via active learning separately. Then the intermediate composition structures of CuxO were selected. In total, 443 iterations of the active learning process were conducted, in which 8.16×10 10 steps were ran from NN-MDs, and 8.30×10 7 structures were compared using two NNPs, and a final dataset with 59491 points was obtained, Table . Once trained, the root mean squared error (RMSE) of the energy (force) is 4. Computational modeling approach for constructing the neural network potential. The data was collected in two stages. In stage 1, the structures of Cu and Cu2O were selected via the active learning procedure, respectively. Based on the dataset of stage 1, the dataset of CuxO was built. In the active learning process, 443 iterations were performed in total, 8.16×10 10 steps were ran from NN-MDs, and 8.30×10 7 structures were checked for extrapolation and interpolation. In the end, the final dataset consists of 59491 points containing 1,801,491 atomic environments and the NNPs are applicable for all three structures.