There are some essential hyper-parameters needed to be pre-defined before the automatic uncertaint-driven AL sampling. In this work, the configuration space for the initial set generation and the uncertainty function optimization is defined in the intermolecular Jacobi coordinates combines with the normal coordinates of H 2 O, (R, θ, ϕ, Q 1 , Q 2 , Q 3 ), where the lower and upper bounds (angstrom for distance and degree for angle) of new grids generations are (2.5, 0.0, 0.0, -0.2621, -0.3078, -0.1801) and (6.0, 180.0, 90.0, 0.1351, 0.2696, 0.1801). This region was chosen according to the reference potential grids and covers the intermolecular potential up to 500 cm -1 . 100 points were randomly generated as the initial set. 1024 points would be optimized to find the local maximums of uncertainty via the "L-BFGS-B" method in scipy, and at most 12 points would be selected as new set. Although the Jacobian matrix is possible to be evaluated analytically, we simply employed the "3-point" As the number of sampling points increases, the test set errors decrease just like the decrease of uncertainty. The test w-RMSEs of the final model of single-precision samplings are 0.3253 cm -1 and 0.3142 cm -1 for the FI-NN and the MLRNet, respectively. These results support the validation of our uncertainty-driven active learning.