For each stage of Li x C 6 compound, we sampled different Livacancy arrangements in Li x C 6 with a total of 1254 structures, and then we calculated the energy and force for each relaxed structure using the rVV10 functional. Next, the atomic charges of each structure in the dataset were labeled with GFN1-xTB. Using this dataset, we trained an initial QRNN model for carrying out active learning loops to sample additional Li x C 6 structures. Each active learning round consists of model exploration, data labeling, QRNN model training, and model stability testing. In the exploration step, we carried out molecular dynamics (MD) simulations of the Li x C 6 compounds with the Atomic Simulation Environment package (ASE) using an intermediate trained QRNN model as the calculator. Please note that the ASE MD was only used for the sampling purpose, and the MD for diffusion calculations was carried out by Desmond (2024-3). We performed ASE NPT MD at 300 and 6000 K for 10 ps using Li x C 6 structures with the number of atoms less than 200. We sampled the structures from the MD trajectories of different stages of Li x C 6 compounds to improve the model's generalizability on all Li x C 6 compounds. In the labeling step, we carried out DFT calculations on the sampled structures to obtain energies and forces, which were added to the existing training dataset. A new QRNN model was then trained with the updated dateset and tested for model stability by running Desmond MD. We found that the model instability is mainly due to spurious graphite structural change from layered to disordered. Therefore, we ran ASE MD at 6000 K to sample the snapshots of the trajectory leading up to the structural change, labeled them with DFT, and added the data the training dataset. After three rounds of active learning, we were able to obtain a QRNN model that is stable for the Li x C 6 system at different Li contents, stage structures and temperatures. In total, we have 3065 entries in the dataset.