Abstract: Artificial intelligence (AI) technologies are reshaping the paradigm of polymer material research and development, especially showing great potential in the field of high-performance polymers such as polyimide (PI). This article systematically reviews the three core applications of AI in polymer science: data-driven molecular generation, performance-oriented PI material design, and the construction of automated experimental platforms. By integrating experimental databases, literature mining, and high-throughput computing, machine learning models (such as graph neural networks and Transformers) have achieved high-precision predictions of multiple properties of PI, including thermal, electrical, mechanical, optical, and gas separation properties. Generative AI (such as VAE and GPT) has further broken through the traditional trial-and-error method, inversely designing virtual PI structures that meet extreme environmental requirements, significantly expanding the boundaries of material exploration. Meanwhile, robotic platforms (such as Polybot and PANDA), combined with Bayesian optimization and active learning, have built a closed-loop system of “design-synthesis-characterization-feedback,” reducing the R&D cycle from years to months. Despite challenges such as data standardization, model interpretability, and the gap between experiments and simulations, AI is still driving PI research and development into a new stage of multi-objective collaborative optimization and cross-scale integration. In the future, it is expected to achieve “customized” polymer material innovation in fields such as 5G/6G and aerospace.

Key words: AI-Driven design, Data-driven molecular generation, Generative design, Multi-property optimization, Self-Driving Laboratory (SDL)