Data-driven prediction of structural changes in polymers induced by radiation

Radiation-induced effects in polymers play a critical role across diverse applications, from nuclear power plant cable insulation to medical equipment sterilization and polymer modification processes. When radiation interacts with polymers, it triggers various effects including oxidation, cross-linking, and chain scission, leading to significant alterations in their chemical, physical, and mechanical properties. Understanding these radiation-polymer interactions is essential for effective polymer design, modification and fabrication strategies. Despite the widespread importance of these effects, the development of machine learning predictive tools has been hindered by the lack of comprehensive and reliable data catalogues. This project aims to address this gap by creating a validated database of polymer-radiation interactions through systematic review of existing literature and targeted experimental work to fill data gaps, ultimately enabling the development of robust database for ML predictive models that can simulate radiation-induced polymer behaviour under various conditions.