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Materials structure, phases and defects for properties and applications

Projects in Progress:

Title Description Research Student Supervisor(s)

(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.

Roman Shantsila (Cohort 5) Bora Karasulu, Albert Bartok-Partay

Machine learning and quantum theory of magnets for energy efficient and renewable Link opens in a new windowenergyLink opens in a new window technologiesLink opens in a new window

Magnetic materials are technologically indispensable - used in motors, generators, solid state cooling, electronic devices, data storage, medical treatment, toys etc. Although the effects of magnetism are easily understood on the macroscopic scale, it has its origins in the complex collective behaviour of the electronic glue, simultaneously binding the nuclei of the material together and generating . In this project we will identify atomistic classical spin models by using machine learning tools on data from calculations of the fundamental quantum mechanics of the electrons. From their study we will such as rare earth metals. The work will relate directly to theoretical work and experimental measurements by International Partners. Laura Cairns (Cohort 4) Julie Staunton;
Albert Bartok-Partay

How amorphous carbon breaks: atomistic models and machine learning

Amorphous carbon (a-C) has many industrial applications, from electrochemical sensors to wear-resistant coatings. Fracture plays a crucial role in the degradation of its performance, with coatings often failing by shear or flexural cracks. This means that as well as being able to predict fracture toughness, it is crucial to understand the response to mixed tensile and shear loads and predict the trajectory of cracks. In this project, we will build on data-driven approaches that use machine learning techniques to produce quantum mechanically accurate models at a fraction of the cost, and use them to produce a complete description of crack growth in a-C. Fraser Birks (Cohort 4) James Kermode, Albert Bartok-Partay

Data-driven modelling of irradiation induced defects in fusion materials

The materials used to build a fusion reactor undergo bombardment from high-energy radiation. In the case of metals, irradiation causes the accumulation of dislocation loops which self-organise into complex microstructures, changing the mechanical properties of the material. To predict this phenomenon accurately, new models are needed. This project will therefore focus on developing a new mathematical framework to connect discrete atomistic models of dislocation loops to continuum differential equations. The resulting modelling hierarchy will be applied computationally to predict the evolution of dislocation loop microstructures, providing an assessment of tungsten's suitability for fusion applications. Joseph Duque-Lopez (Cohort 4) Tom Hudson, James Kermode
Complete thermodynamic description of alloys at extreme pressure and temperature

The aim is to develop a first-principles theoretical and computational scheme that provides a complete thermodynamic description of alloys from ambient to extreme conditions. By 'complete' we mean a technique that is able to compute the Helmholtz free energy of the system, which is then be used to determine properties such as the equation of state and phase boundaries together with properties across boundaries such as latent heats.

Vincent Fletcher Albert Bartok-Partay
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). 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. 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. 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. 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. 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. Matt Nutter (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. 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). Iain Best (Cohort 2) James Kermode