The basic idea behind uncertainty-driven active learning is that, although the initial trainable parameters of NNs are different, the neural networks with the same training set and similar fitting errors should predict almost the same values in a well-sampled region. Therefore, the uncertainty of NN models can be estimated by calculating the (weighted) square energy difference between NN models. By searching a batch of local maximums of the uncertainty function, new point sets can be generated iteratively and be included in the old training set with some pre-defined criterion. Since this method requires only the prediction of a few NN Figure : The basic procedure of the sampling method employed in this work. The initial set is generated by the Sobol quasi-random method in a given configuration space. Then, new points can be added iteratively via the uncertainty-driven active learning method. Some criteria could increase efficiency, such as dropping the points that are too close to each other or have low uncertainty. models (usually two), it is easy to transfer to other ML models or other fitting tasks, making it promising to save points for spectroscopically accurate PES construction.