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

Ginesi, Rebecca ; Hallam Stewart, Fin ; Macdonald, Connor ; Murray, Nicholas

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Measuring self-assembled micelle topologies of functionalised rylenes to build a predictive machine learning model
Autor: Ginesi, Rebecca ; Hallam Stewart, Fin ; Macdonald, Connor ; Murray, Nicholas
Assuntos: BM28 ; SC-5508 ; Soft Condensed Matter Science
Descrição: 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.
Editor: European Synchrotron Radiation Facility
Data de criação/publicação: 2027
Idioma: Inglês

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