The MD-based active learning approach, as shown in Figure , is composed of four steps: initial data generation/collection, training, MD exploration, testing, and labeling. The start of our active learning process is developing an initial database that was used to generate the first set of preliminary DPs. The database contained DFT-MD simulation trajectories and DFT-SP (single point) configurations for SSG and DSG. The complete datapool used to generate the DP for SSG in Achar et al. . was added to this database. There were three main categories of configurations that were specifically generated to sample particular rare events that occurred in graphanol: hydroxyl group rotation (DFT-MD at 800 K), proton hopping (DFT-MD at 800 and 1000 K) and H 2 O dissociation (SP energies of dissociation events). The u24C, 1c24C and 1c24C configurations were used for the three categories, respectively. This SSG database is useful to create a DP for DSG because both materials share similar physics. We explicitly sampled DSG configurations from 36 independent T = 1000 K DFT-MD simulations of 2c24C, where the starting structure for each simulation differed in where the proton was placed and how the hydroxyl groups were oriented. This initial database was then used to generate an ensemble of four DPs, represented by E j ω , j being the DP number and ω being the model parameters. The only difference in the four DPs was the initialization of ω, which leads to the DPs exploring different PESs. The aim of the active learning approach is to minimize the variance from the MD exploration step between the DPs after each iteration. MD exploration was performed by running multiple long LAMMPS N V T MD simulations at varying temperatures using the DP that had the lowest loss function value. The variance was calculated as