A machine‐learning interatomic potential for Si‐C‐N‐H with application to polymer‐to‐ceramic processing of polysilazanes

Abstract We developed a machine‐learning interatomic potential (MLIP) based on Moment Tensor Potentials for atomistic simulations in the Si‐C‐N‐H system. The MLIP was trained on ordered and disordered configurations—including crystalline phases, polymers, amorphous models, and high‐temperature ab initio molecular dynamics trajectories of chemical reactions—and achieves Density–Functional–Theory‐level accuracy for energies, forces, and stresses. We apply the MLIP to complex processes that were previously inaccessible at scale: self‐diffusion in Si 3 N 4 , negative thermal expansion in Si(NCN) 2 , fracture in SiC/Si 3 N 4 composites, and the polymer‐to‐ceramic transformation of polysilazanes. Large‐scale simulations over nanoseconds capture structural evolution during pyrolysis and reproduce experimental observations, such as the formation of nanometer‐sized graphitic segregations in SiCN ceramics. Our results demonstrate that the MLIP extends quantum‐level accuracy to technologically relevant problems, providing new opportunities for predictive modeling of ceramic materials and their transformations.