However, the QCT predictions were still lower than the measured ones because the quantum effect was not considered in QCT. The PES-2018 was fitted to more than 2×10 5 points whose energies were calculated at the CCSD(T)-F12a/AVTZ level. The analytical function form included a many-body term that is described by the permutationally invariant polynomial (PIP) method following the method of Aguado et al. and the resulting fitting error was 1.937 kcal/mol. The performance of the PES is key to determine the dynamic and kinetic properties. The construction of full-dimensional, high fidelity, computationally efficient PESs for chemical systems has been a challenge for a long time in theoretical and computational chemistry. In the last decade, many machine learning methods for developing PESs have emerged, such as the PIPs, PIP-neural network (PIP-NN), and the fundamental invariant-NN (FI-NN). PIP or FI can guarantee the rigorous symmetry property with respect to the permutation of identical atoms, and NN can ensure the high accuracy of the PES. PIP-NN or FI-NN has been successfully employed in fitting full-dimensional accurate PES of many chemical systems based on ample points calculated at the UCCSD(T)-F12a/AVTZ level.