Like other ML-IAMs, these building blocks have historically been fit all at once, yielding ChIMES models for which applications are confined to specific set of atom types and limited to certain compositional ranges. Here, we explore efficacy of the hierarchical transfer learning strategy described above to develop a C/N model valid from near-ambient conditions to extreme conditions of approximately 10,000 K and 200 GPa that is suitable for describing any range of compositions from 0 to 100% N, by building upon previously generated ChIMES models for C (i.e., the 2024-Large model 26 ) and N. We note that this testbed C/N system is also interesting within the context of synthesis of N-doped graphic materials for applications including catalysis, energy storage, and sensing. For example, shock-compression of C/N-rich precursor materials has been shown capable of producing nitrogen-containing graphitic nanoonions on sub µs timescales, which holds incredible promise as a high-throughput strategy for tailored synthesis of exotic and technologically relevant carbon nanomaterials. However, governing phenomena and associated kinetics remain poorly understood due to the extreme associated T and P and far-from equilibrium events that occur. Hence the present pure C/N systems can serve as a reductionist model for understanding this process. Training data were generated through a combination of Kohn-Sham DFT molecular dynamics (MD) simulations and single point calculations. The C/N binary phase diagram is unknown under our conditions of interst; thus, to generate training data, simulations were launched DFT-MD for three different C/N compositions, at a variety of temperatures and densities spanning 300 K/1 g cm 3 to 9000 K/4 g cm 3 as shown in the plot in Figure . Systems at densities below 2.9 g cm 3 were initialized with a graphitic structure, while higher density initial configurations were initialized with a diamond-like structure; N-atoms were then introduced by random substitution. Simulations were run for at least 5 ps; 20 training configurations were taken from each of the 10 simulations to build the initial 298 configuration training set. As is shown in Figure , resulting configurations span graphitic, compressed gas, and high-density liquid, containing both small molecules and larger, polymeric structures. This training set was supplemented with 98 configurations for 3 solid C/N materials, mp-1985, mp-571653, and mp-563, found in the materials project database, comprising cell optimization trajectories under 0, 5, 10, 20 and 40 GPa. Note that the former two structures have been observed experimentally. All DFT calculations were performed using the VASP software package. Interactions were described through the Perdew-Burke-Ernzerhof generalized gradient approximation functional, projector-augmented wave pseudopotentials (PAW), and the DFT-D2 method for description of dispersion interactions, which has previously been shown to be well suited for describing C and N containing materials under extreme conditions. All reported calculations were spin-restricted; we note that, consistent with other studies of C, H, O, and N-containing materials shocked to comparable conditions spin-restricted and -unrestricted calculations yielded similar results. DFT simulations were run in the canonical ensemble with a 0.5 fs timestep, for 5 ps. Electronic eigenstates were occupied according to the Fermi-Dirac distribution with the electronic temperature set equivalent to the target ionic temperature, enabling treatment of ionization and excitation. Simulations cells were selected to be greater than twice the models' outer cutoffs, i.e., large enough to be sampled accurately at the gamma point. We note that all state points with T > 300 K exhibited reactivity; hence, speciation of C, N, N 2 , N 3 , and C 2 N 2 were tracked for those state points.