The E3NN architecture utilized in this work demonstrated high data efficiency in previous work, it requires only 133 structures for fitting water and ice potential surfaces, while other previous neural network (NN) architectures need 140,000 structures to achieve comparable accuracy. Benefit from both the active learning technique and the SOTA NN architecture, we achieved an accuracy of 17 meV for energy and 26 meV/ Å for forces with the NequIP models and our training set was enriched with approximately 78,000 data points sampled within the temperature range of 100 to 800 K. The accuracy of our NN has achieve a chemical accuracy(typically within 43 meV). To further demonstrate the comprehensiveness of our dataset in covering the chemical and conformational space of protein fragments for nonreactive MD simulations, NequIP-based MD simulations were performed on five proteins fragments for 50 ps at 800 K. Figure With the dataset available, we computed all the asymptotic parameters using TDDFT implemented in Psi4, at the PBE0/aug-cc-pVTZ level and in conjunction with the BS-ISA and ISA-Pol algorithms. Although a structural filter was employed to ensure the validity of the conformations within the dataset, a small subset of structures still resulted in nonconvergent BS-ISA calculations, culminating in a final dataset of approximately 74,000 data points. Additionally, for comparison, atomic multipole moments were also computed using DFT calculations at the PBE0/aug-cc-pVTZ level coupled with the Hirshfeld method, as implemented in Multiwfn, ALDA xc kernel was used. Owing to the lower computational demand of the Hirshfeld approach compared to BS-ISA and ISA-pol, Hirshfeld method was used to derive a test set of atomic multipole moments for 20 types of amino acid dipeptides. This test set serves to assess the transferability of the E3NN model, which has been trained on smaller fragments with up to 8 heavy atoms. Further details on CamCASP and Multiwfn calculations are provided in the Supplementary Information.