From figure , we can find that the MLRNet, although containing less trainable parameters, shows better fitting errors than the PIP-NN in almost every sampling cycle. Moreover, the MLRNet achieved better test errors in the first several cycles than the PIP-NN. These facts indicate that the MLRNet has the potential to get a more accurate final model and faster convergency if appropriate sampling methods are employed, and the sampling procedure in this work is less efficient for MLRNet. The most serious problem is that using energy difference as uncertainty may not be appropriate for the MLRNet since its NN outputs are those physical parameters functions D e , R e (and R ref ), and β. This means we may need to define the uncertainty for those MLR parameter functions. Another thing is the unphysical MLR parameter would cause difficulty in the first several iterations. For instance, D e may be less than zero in some poorly sampled regions. We tried to use hyperbolic tangent functions to limit the output to [-1,1], however, this would hurt the fitting ability, and still can't figure out the low convergency problem. However, as the maximum uncertainty of the reference MLRNet PESs is only about 0.05 cm -2 (see table ), we can conclude that the MLRNet with a large fitting set is still reliable.