Models were generated using the ChIMES-LSQ package. Fitting was automated via the ChIMES Active Learning Driver, ALDriver, an open source python workflow tool for automated ChIMES model generation via iterative fitting. The Standard iterative learning strategy coupled with the newly implemented hierarchical learning capability was used for the present work. Briefly, in the iterative learning strategy, an initial (It-1) model is trained to the DFT-MD-generated training set. The model is then deployed in parallel simulations at a selection of the training compositions, temperatures, and densities. Generally, early models are not adequately informed by the available training data and can give rise to unstable simulations that frequently sample poorly informed regions of the model (e.g., near the inner cutoff) and do not conserve the appropriate quantity. Our active learning strategy attempts to select up to 20 such configurations from each simulation, as well as 20 configurations from otherwise stable portions of the simulation. These ChIMES-generated configurations are then assigned labels (forces, energies, and stresses) via single-point DFT calculation, and then added to the training set, from which the next iteration model is generated; this is repeated for a user-specified number of iterative learning cycles. A total of 10 cycles were used in the present work. A weighting factor of w = n I /I is applied to each training point, where n I is the total number of requested iterative learning cycles and I is the current cycle, counting from 1. This has the effect giving DFT-MD generated configurations highest priority weights, which prevents the unphysical configurations generated by early ChIMES models from driving the fit away from relevant physicochemical space. We note that the need for this weighting strategy arises from our desire to generate maximally efficient models, which means that at our target level of model complexity we may not be able to simultaneously describe near and extremely far from optimal structures equally well. Instead, by applying this decaying weighting scheme, we maintain importance of "ground truth" DFT configurations and ensures models converge with subsequent iterations which still adding the benefit of "rare event sampling" and longer-time scales accessible to ChIMES-based MD.