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Alloys

Title Description Supervisor(s)
How semiconductor lasers fail – modelling defect effects

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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. James Kermode, Richard Beanland, Thomas Hudson
Multiscale modelling of precipitation strengthening in superalloys

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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. Peter Brommer
Spanning the scales: insights into dislocation mobility provided by machine learning and coarse-grained models

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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. Albert Bartok-Partay, James Kermode,
Tom Hudson