PhD project: New soft materials using adaptive design

Project description

Soft matter physics has an unmet need for a systematic approach to navigating complex parameter spaces in order to develop new composite materials. The parameters to be chosen usually involve both quantities of different ingredients and processing (e.g. temperature, mixing rate, order of ingredient addition etc.). In this project, you will investigate a new approach to discovering new soft materials to address challenges in personal care, food & drink and energy materials applications by exploiting the strengths of a combined experimental and machine learning approach. Using machine learning as part of an optimisation approach is often called adaptive design. The optimal next experiment is found by balancing a trade-off between exploring uncharted regions of parameter space and moving immediately towards what currently appears to be the best combination of parameters. This is an improvement on more traditional machine learning or statistical inference approaches because it copes better with small data sets and naturally incorporates iterations between lab experiments and computational analysis. In this project, you will use adaptive design to guide the choice of compositional and processing parameters so as to optimise characteristics, for example, the yield of multiple emulsion droplets, the tortuosity of channels in a gel, or the size of clusters of bridged droplets.

Project supervisor

  • (School of Physics & Astronomy, University of Edinburgh)

The project supervisor welcomes informal enquiries about this project.

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