In this work, we use the embedded atom neural network (EANN) to fit the short-range interactions, which are computed by removing the physically-motivated terms from the total CCSD(T) interaction energy. EANN is selected due to its high computational efficiency and its reasonable flexibility to avoid severe overfitting with limited data. Very similar to BPNN and DeepPotential, EANN encodes the local atomic environments within a cutoff distance into feature vectors, then use it to predict the atomic energy. Therefore, EANN cannot sense the interactions beyond the cutoff distance directly, so the locality of the PES is essential for a successful EANN training. In this work, we set the cutoff distance to be a very small value of 4 Å, which essentially only includes the first solvation shell of the central molecule. Highly localized ML potential tends to be more transferrable. Since the number of neighbors scales as