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Atomistic

Materials structure, phases and defects for properties and applications



Available Projects for Autumn 2022 entry

 

For further guidance on how to apply, student funding and the HetSys training programme, please visit the Study with Us page.

Project Title

Description

Keywords

Novel electronic effects for the ultimate thermoelectric energy materialLink opens in a new window

Supervisors:
Phytos Neophytou;
Julie Staunton

Recent advances from condensed matter physics have highlighted a new class of materials whose atomic geometries profoundly affect the nature of their electronic states. The study of these materials has the potential to revolutionize electronics, spintronics, and energy harvesting. Motivated by recent extraordinary experimental measurements and theoretical predictions, this project will investigate the thermoelectric performance of these so-called `topological’ materials, i.e. their ability to convert heat into electricity. The project merges physics and materials engineering, and utilizes DFT and state-of-the-art electronic transport methods. These materials exhibit novel electronic properties with indications for an unprecedented 10-fold performance increase. There is prospect of constituting the ultimate thermoelectric energy-harvesting materials, with enormous contribution to energy savings and net-zero sustainability.

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Quantum, Devices, Materials, Physics, Engineering, Atomistic

Aluminium-steel laser welding: What happens at the interface?Link opens in a new window

Supervisors:
Peter Brommer;
Prakash Srirangam

Aluminium and steel are widely employed metallic materials for automotive applications, such as in vehicle frames. Joining of these two dissimilar metals by laser welding results in formation of brittle aluminium-iron intermetallic compounds at the interface, which degrade the performance of the weld. Here, you will study the joining process through simulations on an atomic scale, directly exploring how iron and aluminium atoms move into the opposite material during and after laser irradiation, supported by transmission electron microscopy analysis. The aim is to find favourable laser weld conditions to mitigate the formation of brittle intermetallics in this technologically relevant process.

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Physics, Engineering, Atomistic, Alloys

How amorphous carbon breaks: atomistic models and machine learningLink opens in a new window

Supervisors:
James Kermode;
Albert Bartok-Partay.

THIS PROJECT IS NOW FILLED

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.

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Quantum, Atomistic, Fracture, Physics, Engineering, Machine Learning

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

Supervisors:
Julie Staunton;
Albert Bartok-Partay

THIS PROJECT IS NOW FILLED

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.

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Physics, Quantum, Magnetism, Atomistic, Machine Learning

Data-driven modelling of irradiation induced defects in fusion materialsLink opens in a new window

Supervisors:
Thomas Hudson; James Kermode

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.

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Nuclear, Quantum, Atomistic, Continuum, Engineering, Mathematics, Physics