PhD Projects Archive

Title	Description	Status	Supervisor	Year
Novel topological effects for the ultimate thermoelectric energy materialLink opens in a new window	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.	Unfilled	Phytos Neophytou; Julie Staunton	2022
Aluminium-steel laser welding: What happens at the interface?Link opens in a new window	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.	Unfilled	Peter Brommer; Prakash Srirangam	2022
Scale effect on reactive turbulent mixingLink opens in a new window	Spatial and temporal fluctuations in fluid behaviour control mixing and reaction processes. Observations show that velocity fluctuations are correlated with mixing and reaction rates, and degree of these correlations vary across the scale. These provide compelling evidence that the key statistics of reaction parameters driving the transport processes are scale dependent and functions of the increments of porous media geometrical characteristics. This project aims to develope theoretical and computational frameworks to assimilate data associated with diverse variables (e.g., velocity, dispersity, reaction rates) collected at a range of scales (from micrometers to kilometres) and combine these to provide predictions of reactive solute dynamics and quantify associated uncertainties across the scales.	Unfilled	Mohad Nezhad; Gary Bending; Tim Sullivan	2022
Biosensing with molecular nanoribbonLink opens in a new windows	DNA sequencing (sensing the order of bases in a DNA strand) is an essential step toward personalized medicine for improving human health. Despite recent developments, conventional DNA sequencing methods are still expensive and time consuming. This project aims to exploit theoretically an alternative strategy for quantum sensing of biological species such as DNA using changes in the electrical properties of a membrane (e.g. molecular nanoribbons containing a pore) upon translocation of biospecies. It will also establish design principles to use molecular nanoribbons for a new generation of quantum devices for selective sensing of biospecies.	Unfilled	Sara Sangtarash; Rebecca Notman	2022
Reliable quantum algorithms for plasma and fusion physicsLink opens in a new window	The field of quantum computation and simulations seeks to develop efficient quantum algorithms for problems that are classically inefficient to solve and are therefore computationally expensive. Furthermore, a quantum-enhanced simulation must not only perform a hard classical simulation efficiently, but also correctly. The latter goal is particularly important as real-world quantum computers are noisy and error prone. This project will develop efficient quantum simulations for problems in plasma and fusion physics, and establish their reliability in real-world quantum computers. The project is ideal for a student interested in a close interplay of quantum computation and simulation with plasma physics.	Unfilled	Animesh Datta; Tom Goffrey	2022
Machine learning based quantum emulators to simulate light-driven catalysis	Industrial catalysis must become sustainable within our lifetime. This means creating renewable fuels and fertilizer to ensure food safety from clean energy such as sunlight and sustainable feedstocks such as atmospheric CO₂ and N₂. To achieve this, we need to be able to understand the mechanisms behind photocatalytic processes and how light excitation can selectively break chemical bonds. This is currently limited by the sheer computational cost of quantum mechanical simulation of light-driven chemistry. The aim of this project will be to create and apply machine learning models that emulate the quantum mechanical interaction between light and molecules at surfaces.	Unfilled	Reinhard J. Maurer; James Kermode	2022
(Truly) Multiscale Simulations of Polymer Crystallization	Understanding the crystallisation of polymers is crucial to improve their functional properties. For instance, the strength-to-weight ratio of Kevlar depends on its degree of crystallinity, which impacts its usage in composite materials such as F1 chassis. However, the current theoretical frameworks for the crystallisation kinetics of polymers suffer from severe limitations when dealing with non-isothermal conditions or the presence of nucleating agent. This project seeks to break new ground by using multi-scale simulations (from molecular dynamic simulations to lattice models) to build a machine learning model for predicting the non-isothermal crystallisation of heterogeneous polymer mixtures.	Unfilled	Gabriele Sosso and Lukasz Figiel	2021
New foundations for electrochemical-mechanical coupling via multiscale simulations	In-service performance of functional materials for energy storage-related applications depends on an intimate interplay between various physical-chemical processes, e.g. strain-dependent ionic transport in polymer electrolytes. Classical electrochemical-mechanical (ECM) models, used to predict the optimum performance of those materials, are based on ad hoc assumptions, omitting the origins of the ECM coupling at the nanoscale. Therefore, the ambition of this research project is to develop new foundations for a holistic computational modelling framework that rigorously captures the ECM coupling by bridging vastly different length scales via multiscale simulations of the material and machine learning. Particularly, the transport of charged species taking place in the presence of stress, electrostatic, and chemical potential gradients will be explored from nano to macro scale.	Unfilled	Lukasz Figiel and Livia Bartok-Partay	2021
Biosensing with molecular nanoribbons	DNA sequencing (sensing the order of bases in a DNA strand) is an essential step toward personalized medicine for improving human health. Despite recent developments, conventional DNA sequencing methods are still expensive and time consuming. Therefore, the challenge of developing accurate, fast, and inexpensive, fourth-generation DNA sequencing alternatives has attracted huge scientific interest. An alternative strategy involves measuring changes in the electrical properties of the membrane e.g. molecular nanoribbons containing a pore. This project will establish design principles to use molecular nanoribbons for a new generation of quantum devices for selective sensing of biospecies such as DNA.	Unfilled	Sara Sangtarash and Nicholas Hine	2021
Complex nanostructured materials for thermoelectric energy harvesting	Two thirds of all energy we use is lost into heat during conversion processes, a loss which puts enormous pressure on the planet and energy sustainability. Thermoelectric materials convert waste heat into electricity and can provide a solution towards this problem. This project studies the electronic and thermal properties of complex electronic structure materials subject to a large degree of nanostructuring, which is believed to be the most promising way to achieve extremely high conversion efficiencies. Density function theory methods, as well as semiclassical and quantum transport methods are merged to explore the design space of new generation thermoelectric materials.	Unfilled	Neophytos Neophytou and Julie Staunton	2021
Machine learning and quantum theory for magnetic quasiparticles and their information storage potential	Some topological magnetic structures can behave as quasiparticles and be manipulated to store information. The discovery of skyrmion-type particles was a huge leap forward opening up the prospect of new smaller and more efficient devices. In this project we will identify atomistic spin models by using machine learning tools on data from calculations of the fundamental quantum mechanics of the electrons and study them to discover what complex but stable magnetic objects can emerge in some choice materials. These developments have wider scope too – for modelling the thermal properties of magnetic phases and their response to applied fields for new solid state cooling applications.	Unfilled	Julie Staunton and Albert Bartok-Partay	2021
Scale effects on reactive turbulent mixing	Spatial and temporal fluctuations in fluid behaviour control mixing and reaction processes. Observations show that velocity fluctuations are correlated with mixing and reaction rates, and degree of these correlations vary across the scale. These provide compelling evidence that the key statistics of reaction parameters driving the transport processes are scale dependent and functions of the increments of porous media geometrical characteristics. This project aims to develop theoretical and computational frameworks to assimilate data associated with diverse variables collected at a range of scales and combine these to provide predictions of reactive solute dynamics and quantify associated uncertainties across the scales. The candidate will benefit from HetSys CDT training on computational data science and geostatistical methods and will perform research in the field of reactive transport and turbulent mixing processes in porous media in collaboration with partners at Bayer A.G. (Germany).	Unfilled	Mohad Nezhad, Gary Bending and Tim Sullivan	2021
Modelling noisy biochemical switches and networks	Biological systems often need to switch behaviour due to environmental (e.g. temperature) and other stimuli (e.g. cell-cell interactions). Yet, understanding how cells can make reliable decisions given the inevitable noisiness of the intracellular conditions remains an open problem. Here, we will develop stochastic models of small biochemical networks that undergo stochastic switching in behaviour. The project involves building a robust software framework for exploring the role of spatial and temporal fluctuations in protein number within different boundary constraints. Working closely with experimentalists (Loose lab, IST-Austria), we will generate predictions that can then be directly tested.	Unfilled	Timothy Saunders and Phillip Stansfield	2021
Advanced Boundary Conditions to Enable Quantification of Uncertainty in Atomistic Simulation of Defects	Accurate models for energy barriers involved in material defect evolution are essential to understand many processes in high performance alloys, for example thermal evolution of radiation damage in nuclear reactor shields. This problem is extremely challenging because it requires both quantum-mechanical precision for the rearrangement of atoms near the defect core and sufficiently large systems to include the long-range elastic response.	Unfilled	James Kermode & Christoph Ortner	2019
Atomistically Informed Fatigue Crack Growth Models	The demanding conditions experienced by welded structures create significant challenges for design and assessment. Due to their reliance on empirical criteria, existing fracture mechanics assessment codes and standards may lead to either over-conservative assessments or the crack tip conditions may be underestimated depending on the nature of the case analysed. The emphasis in this project will be on obtaining more accurate fracture mechanics analyses of fatigue processes through the direct modelling of crack propagation at the atomic scale.	Unfilled	James Kermode	2019
Continuum Models and Inverse Problems	Estimating coefficients of continuum models from data is called an inverse problem. Solutions of inverse problems are not only determined by the laws of physics, but also by the choice of regularisation strategy. The aim of the project is predict the ageing behaviour of Lithium ion batteries by calibrating the regularisation against the data.	Unfilled	Florian Theil	2019
Electronic and phononic transport in thermoelectric materials	Two thirds of all energy we use is lost into heat during conversion processes, a loss which puts enormous pressure on the planet, the use of fossil fuels, and energy sustainability. Thermoelectric materials could be part of the solution, as they convert waste heat into electricity, but their large scale implementation is hindered by low efficiencies and material availability. The project uses atomistic and quantum transport methods to investigate and optimize new generation nanostructured materials with complex electronic and phononic structures, that could boost the thermoelectric figure of merit to unprecedented levels.	Unfilled (TBC)	Neophytos Neophytou & Julie Staunton	2020
Electronic and thermoelectric transport in highly heterogeneous nanometerials and devices	This project studies and designs through theory and large scale simulations, the thermoelectric properties of such materials, their ability to conduct electrons [3], and their ability to stop the flow of heat [3], leading to high conversion efficiencies. Successful high efficiency material designs would enable technological applications that vary from large scale power generation (heat from industrial process, car exhausts, etc.), to micro-scavenging, i.e. to realize self-powered sensor devices that enable the Internet of Things.	Unfilled	Phytos Neophytou	2019
Gone in a flash: Femtosecond laser ablation of nanostructured alloys	Laser ablation, the removal of material with intense light pulses, is an important subtractive manufacturing technique. Femtosecond (10-15 s) laser pulses can result in superior quality of e.g. drilled holes, but they pose a formidable modelling challenge, as the laser drives the electrons in the system to extreme temperatures before the atoms can react. For technologically interesting nanostructured alloys (used e.g. in turbine blades), there are no models that can cope with these conditions. Your task (in collaboration with international partners University of Stuttgart and the openKIM project) is to derive such a model from fundamental data and integrate it into openKIM, a framework for certified simulations on an atomic scale.	Unfilled	Peter Brommer & Albert Bartok Partay	2020
Mathematical Foundations	This project studies and designs through theory and large scale simulations, the thermoelectric properties of such materials, their ability to conduct electrons [3], and their ability to stop the flow of heat [3], leading to high conversion efficiencies. Successful high efficiency material designs would enable technological applications that vary from large scale power generation (heat from industrial process, car exhausts, etc.), to micro-scavenging, i.e. to realize self-powered sensor devices that enable the Internet of Things.	Unfilled	Various	2020
Modelling the Barrier and Elastic Properties of Skin	In this project we will elucidate the molecular structure and organisation of lipid and protein components of the SC by means of molecular dynamics (MD) simulations. This will build on our recent work (e.g. Refs 1-3) and involve making connections between atomistic and coarse-grained MD simulations and macroscale models of skin permeation and of skin’s elastic response, and experimental data. We will achieve a fundamental understanding of the molecular basis for skin disease, skin transport mechanisms and the elastic properties of skin. We will also investigate the effects that other compounds have on these properties.	Unfilled	Rebecca Notman	2020
Modelling the compositional variation of the properties of magnetic refrigeration materials	Refrigeration using magnetic materials has emerged as a promising new, energy efficient and environmentally friendly solid state cooling technology. Cooling cycles are designed to manipulate the changes of entropy and temperature that happen when a magnetic field is applied to align randomly oriented magnetic moments. This project will use a computational approach that models the material at the sub-nanoscale to account for the interactions among the moments and their statistical mechanics to describe the cooling properties quantitatively. We will investigate the effect of compositional heterogeneities, nanostructuring and disorder on the changes of magnetic and structural state of a material, i.e. phase transitions, where the cooling effects are largest.	Unfilled	Julie Staunton, Phytos Neophytou & Tilmann Hickel	2020
Multiscale models for solid-state battery materials	Extreme volumetric expansion, high temperature gradients, and/or high-frequency vibrations, can cause microscale damage phenomena in solid-state batteries, and thus compromise their structural integrity and safety. The objective of this project is to develop multi-scale (micro-to-macro) models that will enable design and analysis of new polymer composite electrolytes with fast-ion conducting particles for future solid-state batteries. Microscale considerations will capture material microstructure and in-situ processes within the electrolyte and surrounding electrodes. Numerical solutions to those microscale problems will generate surrogate microscale models that will be connected with the macroscale via a data-driven homogenisation approach with uncertainty quantification..	Unfilled	Lukasz Figiel	2020
Quantum dynamical simulation of tunnelling and electronic friction: what controls hydrogen chemistry on metals?	Diffusion and reaction of atomic and molecular hydrogen at metal surfaces underpins a wide range of technological applications, including hydrogen dissociation in fuel cells, photoelectrochemical water splitting, hydrogen storage, and heterogeneous catalysis. The small mass of hydrogen means that quantum nuclear effects govern its chemical interaction with metal surfaces. In addition, electronic excitations in the metal can also affect the chemistry via so-called “electronic friction effects”. Recent experiments suggest that there is a rich interplay between nonadiabatic and quantum tunnelling effects, calling for improved theories to provide a mechanistic understanding of these findings.	Unfilled	Reinhard Maurer and Scott Habershon	2020
Uncertainty quantification of long-timescale evolution in precipitation-strengthened alloys	In this project, the ageing process in precipitation-hardened alloys will be studied using kinetic Monte Carlo based on atomic interaction models fitted to electronic structure calculations [3]. Of particular interest is the robustness of predicted evolution pathways, as small errors in the predicted barriers can have severe impact on rates.	Unfilled	Peter Brommer	2019