We employed an active learning approach to develop improved DPs for graphanol with the help of the deep generator (DP-GEN) scheme of Zhang et al. The training contained a series of iterations that was designed to improve the quality of the DP by exploring a larger PES. An ensemble of DPs was trained from the same dataset for each iteration. The only difference in the DPs was the values of the initial NN model parameters (ω). Each iteration generated sufficiently diverse data to minimize the variance between the ensemble DPs after training. Thus, the variance in forces was used as an error indicator (ϵ) to classify DP predictions to be selected for relabeling. A schematic of our DP generation methods is provided as Figure . A detailed description of the data generation, DP training and validation procedures is provided in the Supporting Information.