Generally, several different NN architectures, namely with different I, J and K, are tested to obtain better fittings. For each NN architecture, 100~200 NN trainings were carried out with different initial random parameters. For each NN training, the entire dataset was randomly divided into the training (90%), validation (5%), and test (5%) parts to prevent the "over-fitting" issue. In addition, the fitting was accepted only if all three parts (training, validation, and test) have similar RMSEs, which can efficiently circumvent false extrapolation caused by edge points in the randomly selected validation and test sets.