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Atomistic: Available Projects

The University of Warwick has been awarded £11m to train PhD students in computational modelling.

Available Projects for a September 2024 start

For guidance on how to apply, student funding, the integrated HetSys training programme and what life is like in the HetSys CDT, please visit the Study with Us page.

Note: We are still accepting applications from UK students. The application window for overseas students has now closed.

For details of all our available projects click here.

Project Title Description
Aluminium-steel fusion welding: What happens at the interface?

Aluminium and steel are widely employed metallic materials for automotive applications, such as in vehicle frames. Joining of these two dissimilar metals by fusion welding results in formation of brittle aluminium-iron compounds at the interface, which degrade the performance of the weld. Looking at the motion of individual atoms, we will use modelling techniques such as Molecular Dynamics (MD) and Kinetic Monte Carlo (KMC) combined with transmission electron microscopy analysis to study how iron and aluminium atoms react at the weld interface to find weld conditions where favourable intermetallic compounds form at interface.

Better-conditioned Inverse Problems in Computational Materials Science

Inverse problems are a general class of problems that involve calibrating the parameters of a model using measurements of its outputs, typically from real-world experiments. Many such problems occur across computational science, e.g. in the calibration of constitutive parameters such as elastic moduli (and other examples below) on the basis of computational simulations. However, these problems are often mathematically ill-posed, meaning there is no single, stable, well-defined solution. This issue may be resolved numerically either using classical optimisation approaches which select a single solution (that may be an artefact of the choice of optimizer) or using tools from statistics and machine learning such as Bayesian inference which mitigate the ill-conditioning of the problem by incorporating prior information.

Machine Learning Potentials for Strength Studies in Hexagonal Close Packed Materials

In high-performance applications such as aerospace and medical technologies, there is a high demand for specialised materials with high strength-to-weight ratio and superior corrosion resistance. The reliability and improved development of these materials hinges on our atomic-level understanding on how they behave under stress or strain, and how defects in their crystalline structure affect their performance under different temperature-pressure conditions. This PhD project will take advantage of recent developments in machine learning methods, to enable computer modelling of the mechanical behaviour of titanium alloys to produce a machine learning-based interatomic potential and reference database, as well as to assess its performance in strengths applications.

Filled projects

A Drug Delivery Challenge: Cracking
the Code of Mass Transport in Disordered Systems

Embark on a fascinating journey into the world of drug delivery and mass transport in complex biological systems. This project will explore how molecules navigate through disordered networks, from liposomes to human skin. Led by experts in computer simulations and continuum models, you will unravel the connections between molecular-level interactions and macroscopic drug delivery processes. You will develop skills in both atomistic simulations and fluid dynamics, while addressing challenges such as optimising drug transport and release via state-of-the-art nano-carriers. This project will leverage collaborations with experimental partners and offers a unique opportunity to make a real impact on healthcare applications.

DRUG-THE-BUG: Determining druggable binding sites in bacterial membrane proteins

The bacterial cell envelope is the front-line to killing drug-resistant, pathogenic bacteria. The development of new protein structure prediction methods (e.g. RoseTTA All-Atom AlphaFold 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 Cresset-sponsored PhD studentship (, you will develop and apply methods to identify and characterize binding pockets, predict candidate small molecule and antibody binding, and perform free energy calculations of molecules bound to folded protein structures. The overall aim of this PhD proposal is to develop blueprints for new medicines to treat drug-resistant bacterial infections.

Pushing the boundaries of resolution of biological objects by electron microscopy with GPU-enhanced ptychography

The project aims to enhance Single Particle Analysis (SPA) software, a vital 3D imaging tool in structural biology, by integrating GPU-enhanced ptychography, an advanced computational microscopy technique, into a worldwide SPA framework. Traditional SPA techniques face limitations in resolving small and complex biological macromolecules. By incorporating ptychography, known for its high phase-sensitivity and applicability in low-dose data scenarios, researchers can achieve higher resolution in cryo-electron microscopy. This integration extends the utility of SPA, allowing researchers to work with diverse imaging modalities, significantly elevate data quality, and push the boundaries of resolution. Through international collaboration with leading experts and cutting-edge resources, we aim to advance structural biology, providing the potential for more precise macromolecular reconstructions and overcoming current challenges.

Unlocking Future Photovoltaics: the Effect of Interfaces on Ion Mobility in Perovskite Solar Cells

Solar modules incorporating lead-halide perovskites now exceed the efficiency of conventional silicon modules. However, there is much still to be learned in order to drive further technology improvements. One area of interest is the presence of mobile ionic species, which have been shown to play a role in device efficiency, hysteresis and long-term stability.

The aim of this Oxford PV-sponsored studentship is to investigate the physical and chemical consequences of mobile ions diffusing around interfaces, for example those at grain boundaries and contact layers. This project will make use of ab initio and machine learning techniques to provide in-depth insights that will help the continued development of state-of-the-art solar technologies.

Are you interesting in applying for this project? Head over to our Study with Us page for information on the application process, funding, and the HetSys training programme

At the University of Warwick, we strongly value equity, diversity and inclusion, and HetSys will provide a healthy working environment, dedicated to outstanding scientific guidance, mentorship and personal development.

HetSys is proud to be a part of the Engineering Department which holds an Athena SWAN Silver award, a national initiative to promote gender equality for all staff and students.