Exploration on optimization of synthetic routes for thermal stability of functional materials

This paper constructs a thermal stability synthesis path optimization scheme that integrates multi-objective optimization and deep learning modeling, and theoretically analyzes the influence mechanism of grain structure, interface state, and defect evolution on thermal stability at multiple scales; then, with the help of orthogonal design and response surface method, a synthesis parameter space model is built, and NSGA-II and DNN algorithms are used to achieve comprehensive optimization screening of the "process-structure-performance"path; at the experimental level, in-situ high-Temperature XRD, TEM and Raman techniques are used to verify the microstructure regulation effect, and TG-MS, DMA and thermal shock experiments are used to evaluate the actual effect of thermal stability improvement. The materials under the optimized path are better than the control group in terms of pyrolysis temperature, modulus retention rate and thermal cycle life, and show obvious performance advantages in various engineering applications. The research results show that this method can provide theoretical and technical support for the intelligent synthesis of high-performance thermally stable materials. © 2025 Copyright held by the owner/author(s).