In the PIP-based Δ-learning approach, the root mean square (RMS) bond difference between two configurations was used as the criterion to select points near the equilibrium of stationary points. This strategy can produce a subset that spans the same range of configurations as the original low-level dataset and the correction PES based on a small number of configurations correctly improves the key properties around the equilibrium of the stationary points or along the MEP to a high level. In the PIP-NN based Δ-ML approach, we propose to search the poorly sampled regions of the configuration space according to the limited extrapolation capabilities of NN methods.