Measuring self-assembled micelle topologies of functionalised rylenes to build a predictive machine learning model

This project centres around the creation of machine learning models which will ultimately allow for the prediction of the morphology and ultimately the physical properties of self-assembled aggregates. Due to the mechanisms which lead to molecular self-assembly being poorly understood, the design of new materials for devices is often unachievable. We therefore are developing a model that would predict the morphology of the aggregate from chemical structure alone. In order to generate models capable of predicting the properties of self-assembled aggregates, high-resolution data across a range of compounds needs to be acquired to achieve a prediction with sufficient confidence in order to be useful. This could pave the way for the design of responsive organic materials with the capability of replacing metals in high-value mechanoresponsive devices, amongst other applications.