Theoretical framework and simulation experiment analysis of material organization performance prediction driven by artificial intelligence

With the rapid development of artificial intelligence technology, more and more fields are leveraging its powerful data processing and pattern recognition capabilities for research, and materials science is no exception. Driven by artificial intelligence, this study proposes a material microstructure-performance prediction model. By conducting simulation experiments, it explores the relationship between the material's microstructure and its macroscopic properties. The model predicts and analyzes the mechanical properties, thermal properties, and corrosion resistance of typical metal materials such as steel, aluminum alloys, and titanium alloys, verifying its high accuracy and reliability. The artificial intelligence-driven model can effectively fit test data with high prediction accuracy, and the errors fall within a reasonable range. The combination of artificial intelligence with simulated annealing and genetic algorithms further optimizes the performance of the prediction model.