Title	Description	Status	Research Student	Supervisor(s)
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).	In Progress	Lakshmi Shenoy (Cohort 2)	James Kermode
How semiconductor lasers fail - modelling defect effects	If a dislocation is present in the active volume of a light emitting device, it causes failure by acting as a carrier recombination pathway and grows through the material by emitting atoms, eventually quenching all luminescence. Despite the significant technological progress improved knowledge would generate, the atomistic mechanisms underlying this recombination-enhanced mechanism of 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.	In Progress	Tom Rocke (Cohort 3)	James Kermode, Richard Beanland, Thomas Hudson
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.	In Progress	Adam Fisher (Cohort 2)	Peter Brommer
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.	In Progress	Geraldine Anis (Cohort 3)	Peter Brommer
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.	In Progress	Christopher Woodgate (Cohort 1)	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.	In Progress	Jeremy Thorn (Cohort 3)	Albert Bartok-Partay, James Kermode, Tom Hudson
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.	In Progress	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).	In Progress	Iain Best (Cohort 2)	James Kermode