Work

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

Year: 2025

Type: article

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.

Source: Journal of the American Ceramic Society

DOI: 10.1111/jace.70392

Authors: Shariq Haseen, Peter Kroll

Institution: The University of Texas at Arlington

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,070

Funder: National Science Foundation