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Predicting long-term materials ageing using reaction discovery and machine learning

This project is now filled.

Supervisors: Scott Habershon (Chemistry), Reinhard Maurer (Chemistry)

Predicting the long-term (decades or more) stability of organic polymeric materials under ambient environmental photothermal conditions is a unique challenge because experimental testing on such time-scales is often impossible or too expensive. In this project, we will merge reaction discovery tools (Habershon group) with machine-learning energy calculation methods (Maurer Group) to develop kinetic models to predict the long-term behaviour of organic polymeric materials. These predictive models will then have potential to be used to guide the choice of materials with tailored properties for long-term environmental applications.

Predicting long-term materials ageing using reaction discovery and machine learning