To obtain better optimal fitting, several different NN architectures with two hidden layers have been tested. For each NN architecture, 100 NN trainings were carried out. For each NN training, the sampling dataset was randomly divided into three parts, namely, the training (90%), validation (5%), and test (5%), to avoid the so-called "over-fitting" issue. In addition, the fitting was adopted only if all three sets (training, validation, and test) have similar RMSEs. Meanwhile, the maximum deviation was also considered for choosing the final optimal PES.