In the following sections, we provide an overview of our approach within the context the ChIMES ML-IAM, followed by discussion of our target application and initial training data generation strategy. We apply our approach to a testbed system comprising mixtures of carbon (C) an nitrogen (N) under conditions ranging from nominally ambient temperature (T ) and pressure (P ) up to 10,000 K and 200 GPa. We note that this testbed was chosen due to the relatively low atomic complexity (i.e., only two species are present) but high configurational complexity (e.g., including multiple phases, compositions, and chemistry). Performance of transfer-and direct-learned models are compared with DFT, and results are discussed within the context of the following guiding questions: