Designing Radiation-Resistant Polymers Using Generative AI: A Breakthrough Application of Machine Learning in Materials Science

This article introduces a closed-loop framework based on generative AI for the inverse design of radiation-resistant polymers. The system combines a random forest surrogate model and a conditional Transformer network, significantly improving the efficiency of new material discovery and bringing breakthrough applications to the field of materials science.

In fields such as aerospace, nuclear energy industry, and deep space exploration, materials need to withstand high-intensity radiation environments. Traditional radiation-resistant polymer development relies on trial-and-error experiments and empirical rules, requiring testing of a large number of chemical structures, which is time-consuming and inefficient.

The core of the project is an innovative dual-system architecture:

1. The random forest surrogate model predicts polymer properties, with a coefficient of determination (R²) exceeding 0.90 for glass transition temperature (Tg) and over 0.99 for mechanical properties (MAC R²);
2. The Transformer-based conditional generative model can generate new polymer molecular structures that meet conditions based on parameters such as radiation resistance level and thermal stability.

Compared with traditional brute-force search methods, the AI-driven system has a hit rate 3.7 times higher; the validity of the generated molecular structure SMILES is over 98%, ensuring the chemical structure is practically feasible.

This technology shortens the R&D cycle of new materials and provides new ideas for material problems in special environments:

• Protecting electronic devices from cosmic ray damage in spacecraft manufacturing;
• Enhancing safety and durability in nuclear energy facilities;
• Can be extended to other material design tasks such as conductive polymers, biocompatible materials, or self-healing materials.

Using generative AI for inverse material design is an important milestone in the integration of materials science and AI. With algorithm optimization and improved computing power, more high-performance materials will be designed quickly, and the interdisciplinary cooperation model may completely change the traditional material R&D paradigm, accelerating the application process of advanced materials.