Project Title

Description

Understanding how molecules move through disordered networks is a fundamental question that has an impact on countless practical applications. For instance, many medicinal drugs are delivered through our skin - a very complex system. Another example is the percolation of ice through plant cells, which can massively reduce crop yield. Until now, the modelling of these applications has relied on either continuum models or molecular simulations. With this project, we will build a bridge between the two disciplines, thus developing an understanding of the relationship between (macroscopic) mass transport and the (microscopic) structure of biological disordered materials.

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Soft-matter systems exhibit an extremely rich macroscopic behaviour, with complex and fascinating phase properties. Many of these exotic structures and transitions can be captured by relatively simple hard-core soft-shell interaction potentials in simulations, however, our precise understanding is often obscured by difficulties in sampling the configuration space exhaustively. Linking such potentials to specific macroscopic properties requires many expensive simulations, limiting insight into how the form of the potential determines phase behaviour. In this project we will adapt a novel data-intensive sampling algorithm to make automated predictions of structural and thermodynamic properties. We will then exploit the new algorithm to reverse engineer models, using machine learning methods, that capture novel phase behaviour of specific soft-matter systems by design rather than discovery through brute-force trial and error.

(Inter)facing the Bitter Truth: How to Design Better Interfaces in Next-Gen Batteries using Atomistic Simulations Assisted by Machine-Learning

Lithium-Sulphur batteries (LSBs) are a promising alternative to Li-ion batteries (LIBs) as a next-gen energy storage technology, providing higher theoretical capacity at lower costs. Replacing the conventional liquid electrolytes with solid electrolytes (SE) helps mitigate the major LSB issues like the Li-polysulfide shuttle effect, and safety risks. Current SEs, however, degrade when coupled with a S-cathode, impeding the Li-ion conduction across their interfaces, limiting the battery performance. To design superior SE/S-cathode interfaces, this project focuses on atomistic simulations of the interfacial sulphide conversion chemistry in LSBs utilising state-of-the-art Density Functional Theory and machine learning methods, providing insights that are otherwise elusive to experimental characterisation techniques.

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Identifying druggable binding sites in computationally-determined models of bacterial membrane proteins

Supervisors:
Phillip Stansfeld (Life Sciences\Chemistry), Livia Bartok-Partay (Chemistry)