Fig. 1: Workflow for deep potential (DP) construction using GeNNIP4MD.

a The cycle begins with the creation of an initial training dataset. b In the training step, four DPs with different initial parameters are trained. c The predictive accuracy of the trained DPs is evaluated. d If the accuracy is insufficient, additional training data are generated to improve the model. First, a diverse and large set of candidate atomic structures is generated using molecular dynamics (MD) with the current DPs. In the subsequent screening step, structures are selected based on the maximum force deviation among the DPs, within a predefined range. This deviation threshold is set to effectively identify poorly learned structures with large prediction errors. The structural similarities of the selected candidates are then evaluated relative to the current training set, and structures with low similarity are chosen for labeling. In the labeling step, density functional theory (DFT) calculations are performed on the selected structures, and the resulting data are added to the training set. This cycle is repeated until a DP with satisfactory accuracy is obtained. (RMSE: root mean square error).