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PhD Projects in Progress

Cohort 1 (2019 entry)

Name Description Theme Research Student Supervisor
A Game of Order Parameters Predicting if, how and when ice crystals will form in clouds (and our own cells!) is important to atmospheric science (and cryopreservation!) and manufacture of pharmaceuticals. Medicine Katarina Blow (Cohort 1) David Quigley & Gabriele Sosso
Accelerating Theoretical Spectroscopy through Machine-learning Theoretical approaches to predicting and interpreting advanced spectroscopy techniques for investigating energy and charge transfer processes at the nanoscale will be modelled and accelerated with machine-learning tools. Device Carlo Maino (Cohort 1) Nicholas Hine
Atomistically-informed continuum interface models for functional composites Functional composites are material candidates for high-energy density applications. Their overall energy density can be enhanced by tailoring constituent dielectric properties, breakdown strength, and interfacial polarisation. Composites Aravinthen Rajkumar (Cohort 1) Lukasz Figiel
Automatic Prediction and Characterisation of Complex Chemical Reactions Around 90% of all chemical processes use catalysis to control reactivity and selectivity, yet the design of new catalysts too often depends on informed trial-and-error to make progress. Medicine Idil Ismail (Cohort 1) Scott Habershon
Coupling fluid and kinetic codes for laser-driven inertial fusion energy simulations Coupling kinetic solutions of laser-plasma interactions to large-scale fluid simulations will help optimise experiments aimed at achieving thermonuclear fusion driven by lasers Fusion Andrew Angus (Cohort 1) Tony Arber, Keith Bennet & Tom Goffrey
Fluctuating Hydrodynamics for Liquid Spreading over Heterogeneous Surfaces Understanding the spreading of liquids over heterogeneous solid surfaces is the key to numerous emerging technologies (e.g. 3D ‘metaljet’ printing) and biological systems (retention of rain by leaves). Fluids Jingbang Liu (Cohort 1) James Sprittles & Duncan Lockerby
Physics of magnets and the arrangements of atoms comprising them Permanent magnets are widespread - key components in motors and generators, transducers, imaging systems etc. Their fundamental materials physics is also fascinating and challenging. Alloys Christopher Woodgate (Cohort 1) Julie Staunton
Uncertainty Assessment of Solute Mixing in Heterogenous Porous Media The transport of chemical substances in the subsurface is relevant in many different applications. Composites Matthew Harrison (Cohort 1) Mohaddeseh Mousavi-Nezhad

Cohort 2 (2020 entry)

Name Description Theme Research Student Supervisor
Adaptive probabilistic meshless methods for evolutionary systems This project will develop and implement a new class of numerical solvers for evolving systems such as interacting fluid-structure flows. To cope with extreme strain rates and large deformations these new solvers will be adaptive and meshless, and they will also implicitly represent their own solution uncertainty, thus enabling optimal design and uncertainty quantification. This exciting project brings together aspects of continuum mechanics, numerical methods for partial differential equations, and statistical machine learning. Fluids Tadashi Matsumoto (Cohort 2) Tim Sullivan
Atomistic modelling of fracture for irradiated materials Reactor pressure vessel (RPV) steels used in nuclear power plants have very complex behaviour due to the large number of alloying elements. Irradiation effects affect the flow of impurities towards grain boundaries, modifying solute segregation and leading to embrittlement and reduced operational performance. This project is part of a European consortium developing a multiscale model for embrittlement. The PhD project targets one of the last remaining gaps within the multiscale modelling of irradiated materials: linking neutron irradiation to variation of mechanical properties. The model will be validated by experiments carried out by partners at EDF and CEA (both in Paris). Alloys Lakshmi Shenoy (Cohort 2) James Kermode
Computational Modelling of Leidenfrost Fractals The Leidenfrost effect levitates small liquid drops above hot surfaces via a strong evaporative flux and is seen when water droplets skate across a hot pan. Understanding when this occurs is critical for the efficiency of numerous technologies, including the spray-cooling of next-generation electronics that our collaborators at Nokia Bell Labs are developing. Experiments at Bell have recently revealed counter-intuitive contact dynamics that display fractal surface wetting in competition with the nanoscale vapour flow under impacting droplets. This project will use unconventional mathematical modelling combined with computational techniques to gain unprecedented insight into this phenomenon combined with theory-driven targeted experiments at Bell. Fluids Peter Lewin-Jones (Cohort 2) James Sprittles
Investigating the impact of equation of state uncertainties on direct-drive inertial fusion energy simulations Direct-drive inertial fusion energy1 (IFE) requires high-energy lasers to be focussed on a spherical target. The outer material of the target (usually plastic) ablates, driving an implosion of the core deuterium-tritium (DT) fuel. To design efficient future experiments and interpret previous ones an accurate and predictive computational modelling capability is required. This must include a formal understanding of the sources and magnitudes of uncertainty. This project will investigate the uncertainty in direct-drive IFE calculations arising from the equations-of-state used. The primary outcome of the project will be an uncertainty quantification (UQ) framework that could also be applied to other areas of uncertainty, such as opacity, emissivity and thermal transport. Fusion Charlotte Rogerson (Cohort 2) Tom Goffrey
It’s all in the Structure: Transforming drug design by bringing together molecular simulations and machine learning The solubility of pharmaceutical drugs determines to what extent they can be absorbed. Machine learning algorithms can predict the solubility of novel drugs without the need of actually synthetizing them - thus saving substantial time and money. However, we currently infer solubility from the structure of single molecules in vacuum - a sub-optimal approach ignoring interatomic interactions. This project, supported by AstraZeneca, will address this pitfall by generating three-dimensional molecular models of crystalline drugs polymorphs and simulate their dissolution by means of enhanced sampling simulations. These results will be used to construct a machine learning framework that will unravel the atomistic origins of drugs solubility. Medicine Steven Tseng (Cohort 2) Gabriele Sosso
Modelling the extraordinary strength of superalloys The extraordinary strength of superalloys (used e.g. in aeroplane engines) is caused by nanoscale precipitates formed in an ageing process. This process covers timescales from femtoseconds up to seconds and beyond, which poses a formidable modelling challenge. Isolating rare events where atoms actually move from thermal vibrations around their equilibrium position speeds up the simulation to allow studying the precipitate formation process with a view to understanding and potentially improving it. Of particular interest is the robustness of the predicted precipitation pathways to uncertainties in the atomistic model used. This project is co-funded by our industrial partner TWI. Alloys Adam Fisher (Cohort 2) Peter Brommer
Nanoscale material discovery for thermoelectric energy harvesting and cooling This project aims to exploit electronic and vibrational properties of nanoscale materials that are 1000 times smaller than diameter of a human hair to discover new materials for energy harvesting and cooling in consumer electronics such as mobile phones and laptops. The ultimate goal of the research is to understand quantum and phonon transport through molecular structures for thermoelectricity and thermal management. The candidate will receive a broad training on computational materials modelling and gain experience with cutting edge quantum transport simulation methods, conduct a vibrant research with publication potential and would have an opportunity to conduct collaborative projects with internationally leading experimental groups in Europe and beyond. For more information contact Dr. H Sadeghi ( or visit Device James Targett (Cohort 2) Hatef Sadeghi
Next generation sampling of organic molecules Models of simple flexible organic molecules, such as methane or carbon dioxide, have surprisingly rich phase diagrams showing numerous (and sometimes spurious) stable and metastable crystal structures. These models are of utmost interest in areas from organic electronics to pharmaceuticals. Traditional computer modelling of these systems requires laborious application of multiple techniques. We will instead adapt a novel “one shot” data-intensive algorithm (nested sampling) which makes both structural and thermodynamic predictions, to flexible organic molecules. We will use the resulting tool to automatically calculate full phase diagrams, initially for simple “toy” alkane models and ultimately realistic heterogeneous organic systems. Medicine Omar Adesida (Cohort 2) Livia Bartok-Partay
Predicting long-term materials ageing using reaction discovery and machine learning Predicting the long-term (decades or more) stability of organic polymeric materials under ambient environmental photothermal conditions is a unique challenge because experimental testing on such time-scales is often impossible or too expensive. In this project, we will merge reaction discovery tools (Habershon group) with machine-learning energy calculation methods (Maurer Group) to develop kinetic models to predict the long-term behaviour of organic polymeric materials. These predictive models will then have potential to be used to guide the choice of materials with tailored properties for long-term environmental applications. Medicine Joseph Gilkes (Cohort 2) Scott Habershon
Statistics of porous media attributes and mixing processes state variables across scales Typical observations of porous media attributes arise from a variety of techniques which have their own spatial resolution and associated uncertainty. Therefore, key statistics of parameters driving transport processes in porous media vary across scales and a scientific foundation for the characterization of basic transport mechanisms requires understanding of all relevant processes across the relevant length and time scales. In this project, we develop a theoretical and computational framework to assimilate data associated with diverse variables collected at a range of scales and combine these to provide predictions of solute dynamics and associated uncertainties. Composites Alisdair Soppitt (Cohort 2) Mohaddeseh Mousavi-Nezhad
Step into the unknown: modelling titanium alloys at extreme conditions Titanium alloys are very popular in industrial and medical applications due to their excellent mechanical and chemical properties. Among these the ternary alloy containing 6% aluminium and 4% vanadium is the most commonly used, yet little is known about the microscopical mechanisms that stabilise the alloy. Lack of insight makes it challenging to predict properties at extreme conditions, such as high pressures and temperatures near the melting point. To perform realistic computer simulations on the atomistic scale probing the uncharted territory of the phase diagram, you will develop a machine-learning accelerated model and apply it in large scale calculations. Alloys Connor Allen (Cohort 2) Albert Bartok-Partay
Uncertainty in phase diagram simulations with interatomic potentials Atomistic simulations with interatomic potentials are very widely used throughout computational chemistry, physics and materials science. Currently many important processes are beyond the reach of quantum mechanical methods such as density functional theory; only empirical potentials can reach the necessary microstructural length scales and extended time scales. Currently it is almost impossible to put meaningful error bars on the output of complex atomistic simulations. This PhD project will address this challenge by relating simulation outcomes to the form and parameters of the potential, in collaboration with partners Ralf Drautz (ICAMS, Bochum, Germany) and Ryan Elliot (OpenKIM project, U. Minnesota, USA). Alloys Iain Best (Cohort 2) James Kermode

Cohort 3 (2021 entry)

Name Description Theme Research Student Supervisor
2D Material Heterostructures and novel Twistronic Devices This project explores the exciting and novel physics of multilayer structures built from 2D materials. 2DMs can undergo dramatic changes in their fundamental physical properties when they are combined into heterostructures, particularly if their lattices are misaligned: an example is how graphene becomes superconducting when the alignment of two layers is “twisted” by specific angles of a few degrees. This new field of “twistronics” explores how properties of 2D materials can be tailored for specific applications by stacking them together. We will harness unique capabilities of Linear-Scaling DFT to design 2DM heterostructures “ab initio” for future application in electronic devices that combining high performance and ultra-low power usage. There will be opportunities to use Machine Learning tools to accelerate these simulations, and to develop theoretical spectroscopy methods that enable prediction and interpretation of state-of-the-art experimental results. Device Anas Siddiqui (Cohort 3) Nicholas Hine and Neil Wilson
Exploring the Heterogeneous Biogenesis Pathways of the Bacterial Cell Envelope The cell envelope serves as the front-line to both defence and pathogenicity in bacteria. Critical protein assemblies are required to mature, localise and assemble proteins, sugars and lipids around the cell to enable protection against antibiotics, phages and toxins, and to modulate cell structure and shape. A major component of this is the essential extracellular cell wall, which forms a mesh-like coat around the cell. Its assembly is the target for many antibiotics. Here we will use molecular simulation to study the protein machinery responsible for the formation of the cell wall and other biogenesis pathways within the cell envelope. Medicine Matyas Parrag (Cohort 3) Phillip Stansfield and David Roper
How semiconductor lasers fail - Modelling defect effects If a dislocation is present in a light emitting device, it causes failure by acting as a carrier recombination pathway and grows through the material by emitting atoms, eventually shutting of all luminescence. This is a long-standing and significant problem for industrial applications; however, despite the significant technological progress improved knowledge would generate, the atomistic mechanisms underlying recombination-enhanced dislocation climb and its interaction with vacancies and interstitials are poorly understood, with no first principles work reported to date. This PhD project will address this deficiency for the first time using simulation methods that combine quantum mechanical (QM) calculations with machine learning. Alloys Thomas Rocke (Cohort 3) James Kermode and Richard Beanland
Multiscale modelling of precipitation strengthening in superalloys The extraordinary strength of superalloys is derived from precipitates – nanoscale inclusions embedded in the material. These strengthen the material by hindering the motion of dislocations, which are responsible for material deformation. The precipitates typically come in a distribution of shapes, sizes, orientation, etc. This project explores the effect of these variations on the properties of the material. A better understanding of precipitates will lead to rational criteria for the design of new high strength, low weight alloys that would increase the efficiency of turbine products and new engine designs. Alloys Geraldine Anis (Cohort 3) Peter Brommer
Nonlinear, but under control: a hierarchical modelling approach to manipulating liquid films We are surrounded by situations that depend on a controlled outcome in our day-to-day lives, ranging from controlling the evacuation of crowds, to efficient drug delivery, or cooling systems inside computing centres. Most real-life scenarios rely on complicated models which are too complex to tackle analytically or computationally. Using the framework provided by a beautiful and rich physical problem – controlling nonlinear waves in falling liquid films – the project will provide opportunities to develop analytical and computational multi-physics tools. Acting in tandem for the first time, they become sufficiently powerful to translate robust theoretical strategies into realistic technological solutions. Fluids Oscar Holroyd (Cohort 3) Radu Cimpeanu and Susana N Gomes
Protein origami: New computational methods to predict protein folding ensembles How does a protein fold into its native state? This is one of the most important and challenging problems in the chemical sciences, and a key question in understanding diseases driven by protein misfolding and aggregation (such as Parkinson’s disease). In this project, we will develop and employ a new computational scheme to access long time-scale protein folding events by mapping onto a discretized connectivity-based description of protein structure. This new approach will then enable us to investigate sequence-specific folding effects and translational folding, as well as providing a new scheme for protein-structure prediction. Medicine Ziad Fakhoury (Cohort 3) Scott Habershon and Gabriele Sosso
Simulating Surface Spectroscopy of Single Atom Magnets and Catalysts Functional materials for catalysis and magnetooptical applications often contain precious materials, the scarcity of which is a strong motivation for maximizing the efficiency of their use. Single atoms can act as catalysts (Single atom catalysts, SACs) or single atom magnets (SAMs) when stabilized on well-defined substrates. SAC/SAM materials are often studied with X-ray photoelectron spectroscopy and X-ray absorption spectroscopy, but the rich structure and overlapping features make these spectra hard to disentangle. This project will develop new simulation methodology to predict such complex spectroscopic signatures and, in collaboration with experimental partners, novel SAM and SAC materials will be characterized. Device

Dylan Morgan (Cohort 3)

Reinhard Maurer and Julie Staunton
Spanning the scales: insights into dislocation mobility provided by machine learning and coarse-grained models How do metals break? How can we make them stronger? What are the roles of defects and impurities? The strength of materials are ultimately determined by the microscopic interactions on the atomic level, which can be modelled accurately. However, the challenge is that computationally it is not possible to propagate information in one step from the nanometer to the millimeter scale. In this project, you will use combined Quantum Mechanics-Molecular Mechanics and Gaussian Approximation Potentials, a machine learning approach, to develop coarse-grain models of dislocations and to make quantitative predictions of plastic deformations in metals and alloys. Alloys Matt Nutter (Cohort 3) Albert Bartok-Partay, James Kermode and Tom Hudson
Untangling disorder in amorphous pharmaceuticals using artificial intelligence The best pharmacologically active compound may have been found, but its function, properties and processability are heavily dependent on the solid form. Solid-state Nuclear Magnetic Resonance and diffraction experiments, coupled with quantum mechanical calculations are powerful tools to elucidate the atomic structure, which is needed to understand and control macroscopic properties. The challenge in amorphous systems is that experiments only provide globally averaged measurements, while large length scales are needed to capture the disorder, which render quantum mechanical calculations unfeasible. In this project, machine learning models will be used to bypass the need for these costly computations, allowing interpretation of experimental data and structure determination with unprecedented accuracy. Medicine Jeremy Thorn (Cohort 3) Albert Bartok-Partay and Steven Brown
Using surrogate models to optimise designs for laser-driven fusion power production Laser-plasma experiments in high-energy density physics (HEDP) and fusion research trigger kinetic scale instabilities whose effects must be included in large-scale fluid simulations. The difference in time and spatial scales between the kinetic and fluid models, along with the cost of the kinetic modelling, have hindered the full inclusion of important kinetic processes in laser-driven fusion simulations. It is critical for the design and interpretation of experiments that this is solved. This project aims to develop a surrogate model for the kinetic processes suitable for including in fluid scale simulations. Fusion Ben Gosling (Cohort 3) Tony Arber, Tom Goffrey and Keith Bennett.