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Atomistic: Available Projects

Materials structure, phases and defects for properties and applications

Available Projects for Autumn 2023 entry

For further guidance on how to apply, student funding and the HetSys training programme, please visit the Study with Us page.

Project Title


Multiscale Modeling of Mass Transport in Disordered Media: from Drug Delivery to Freezing Tolerance

Gabriele Sosso (Chemistry), James Sprittles (Maths)

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.


Biomolecular modelling; Materials characterisation; Mathematical and statistical foundations; Medicines
Reverse engineering models for soft matter systems

Livia Bartok-Partay (Chemistry), David Quigley (Physics), James Kermode (Engineering)

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.

Materials characterisation

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

Bora Karasulu (Chemistry), Albert Bartok-Partay (Engineering\Physics)

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.


Materials characterisation; Mathematical and statistical foundations

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

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

The bacterial cell envelope is the front-line to killing drug-resistant, pathogenic bacteria. The development of AlphaFold2 and ESMFold have enabled the accurate computational determination of over 600 million protein structures. This dataset enables the study of entire bacterial membrane proteomes from the perspective of structure-based drug discovery. As part of this proposal, we aim to develop and apply methods to identify and characterize binding pockets, dock candidate small molecules and perform free energy calculations to investigate small molecule binding to folded protein structures. Our overall aim is to develop blueprints for new medicines to treat drug-resistant bacterial infections.

Biomolecular modelling; Medicines