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PhD Project Opportunities 2022

Available Projects 2022/23

We are pleased to announce that our projects starting in the 2022-23 academic year are open for applications. Please see below for details.

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

At the University of Warwick, we strongly value equity, diversity and inclusion, and HetSys will provide a healthy working environment, dedicated to outstanding scientific guidance, mentorship and personal development.

HetSys is proud to be a part of the Physics Department which holds an Athena SWAN Silver award, a national initiative to promote gender equality for all staff and students. The Physics Department is also a Juno Champion, which is an award from the Institute of Physics to recognise our efforts to address the under-representation of women in university physics and to encourage better practice for both women and men.

Project Title Description Keywords Theme
Fundamental physics or data science? Why not both: a data-driven modelling framework for interfacial microflows

Supervisors:
Radu Cimpeanu; James Sprittles; Albert Bartok-Partay

This exciting project lives at the interface between multi-physics modelling, high performance computing and data-driven approaches. The 21st century has brought a revolution in micromanufacturing techniques (LCD, 3D printing etc.) that require understanding and efficient deployment of knowledge at scales below those currently accessible. Enter data-driven equation discovery techniques: novel surrogate modelling methods which can provide insight in scenarios in which simulation or experimental data are available, but traditional derivation approaches break down. Our challenge is to create a new computational framework that harnesses the power of these approaches towards generating new meaningful understanding of fluid flows at small scales.

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Engineering, Mathematics, Continuum, Fluids, Solids Continuum

Optimising power grids and chemical reactions with graph neural networks

Supervisors:
Albert Bartok-Partay;
Reinhard J. Maurer

In this project the wider applicability of graph neural networks will be explored, to answer questions as "How do atoms arrange in space to form molecules and materials?" and "How does power flow in an electrical grid?". The common theme is that both problems may be represented as graphs: atoms or substations as the vertices, and bonds or transmission lines as the edges. GNNs will be employed to model interactions in such systems and to optimise processes. Results of this work will be useful in optimising electricity grid operations and schedules as well as in understanding chemical transitions between different molecules.

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Physics, Chemistry, Engineering, Molecules, Machine Learning, Neural Networks Quantum

Biosensing with molecular nanoribbons

Supervisors:
Sara Sangtarash;
Rebecca Notman

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.

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Quantum, Devices, Transport, Engineering, Physics, Chemistry, Atomistic Quantum

Harnessing Molecular Simulations to advance Electronics and Photovoltaics: design rules for the selective deposition of metals by novel condensation techniques

Supervisors:
Gabriele C. Sosso; Ross Hatton

Copper and silver are key to electronics and photovoltaics. However, depositing in a controlled manner these metals on a given surface is a slow and costly process. We have recently discovered that a thin layer of specific organofluorine compounds enables the selective deposition of copper and silver. This unconventional approach is fast and inexpensive, but our understanding of its molecular-level details is presently very limited. This project will investigate the interplay between the metal-organofluorine interaction strength and the polymer-polymer intermolecular interactions by combining density functional theory and classical molecular dynamics - thus identifying concrete design rules to further electronics and photovoltaics.

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Atomistic, quantum, chemistry, molecules, electronics, photovoltaics

Quantum

Applying Machine Learning to understand photoprotection: how do triazine-based UV-filters really work?

Supervisors:
Nicholas Hine; Vas Stavros

Ultraviolet radiation (UVR) has far-reaching consequences on life such as skin cancer in humans and damage to photosynthetic machinery in plants. This project will study the fundamental mechanisms that provide naturally-occurring molecules with photoprotective properties, allowing them to absorb UVR and dissipate it harmlessly as heat. We will harness the power of Machine-Learned Potential Energy Surfaces to accelerate accurate-but-slow electronic structure calculations based on Time- Dependent Density Functional Theory. This will enable calculations of properties such as excited state lifetimes in a complex solvent environment. One main target of this will be triazine-based UV filters found in commercial sunscreen formulations.

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Quantum, Atomistic, Chemistry, Molecules, Machine Learning Quantum

Novel electronic effects for the ultimate thermoelectric energy material

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 Atomistic

Reliable quantum algorithms for plasma and fusion physics

Supervisors:
Animesh Datta; Tom Goffrey

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.

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Quantum, Plasma, Physics, Mathematics, Continuum Quantum

Aluminium-steel laser welding: What happens at the interface?

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 Atomistic

How amorphous carbon breaks: atomistic models and machine learning

Supervisors:
James Kermode;
Albert Bartok-Partay.

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

Atomistic

Machine learning based quantum emulators to simulate light-driven catalysis

Supervisors:
Reinhard J. Maurer;
James Kermode

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.

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Catalysis, Quantum, Chemistry, Materials, Molecules, Physics, Machine Learning

Quantum

When the dust settles: predicting deposition of particulate and aerosols

Supervisors:
Duncan Lockerby; James Kermode

Predicting the deposition of ultra-fine particulate and aerosolized drops is important in a wide range of applications: from understanding pathogen or drug-laden droplet deposition in the respiratory systems to determining the composition of airborne particulate matter using environmental sensors. The physics at play is diverse, and its accurate prediction requires a multi-scale and multi-physics model, far beyond the current state-of-the-art. Such a model will combine techniques from fluid dynamics, kinetic theory, Langevin dynamics, and uncertainty quantification, and tackle a broad range of physics including rarefied gas dynamics, creeping flow, micro-scale evaporation, and Brownian motion.

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Fluids, Physics, Engineering, Mathematics, , Bayesian Inference

Continuum

Artificial Intelligence (AI)-enabled Cryogenic Electron Ptychography For Bio-macromolecule Imaging

Supervisors:
Peng Wang

Ptychography is an emerging computational microscopy technique for acquiring images with resolutions beyond the limits imposed by lenses. It has been applied to high-resolution X-ray imaging in synchrotrons and accurate wavefront-sensing in space telescopes. Instead of macroscopic objects, our work aims to visualise the basic building blocks of life (e.g. proteins) in 3D towards a near-atomic resolution by developing ptychography in conjunction with Nobel-prize winning cryogenic electron microscopy (cryo-EM). Our analysis will be enhanced by artificial intelligence and machine techniques developed in this project. We will work closely with experimentalists at the medical research centre of the Rosalind Franklin Institute, and structural biologists at the University of Oxford.

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Physics, Life Sciences, Machine Learning, Imaging Quantum

Machine learning and quantum theory of magnets for energy efficient and renewable energy technologies

Supervisors:
Julie Staunton;
Albert Bartok-Partay

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.

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Physics, Quantum, Magnetism, Atomistic, Machine Learning Atomistic

Hopping through the interfaces: a multiscale chemo-mechanics model for energy materials

Supervisors:
Lukasz Figiel;
Bora Karasulu

Mechanical damage arising from electrochemical processes in energy materials can alter significantly their mass transport capability, and overall performance of energy storage systems. The damage is frequently initiated at material’s internal interfaces, subsequently disrupting ionic and electronic conductivity paths. The coupling between interfacial damage and ionic transport is not yet fully understood, and requires description of its origins at the nanoscale. This project will provide enhanced understanding of the damage-transport coupling for various interfaces in energy materials across the length scales by developing a novel data-driven multiscale methodology based on the Bayesian inference, linking first-principles calculations with the continuum modelling framework, and subject to physical constraints

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Chemistry, WMG, Chemomechanics, Multiscale, Continuum Continuum

Dislocation modelling for fusion applications

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 Atomistic

Scale effect on reactive turbulent mixing

Supervisors:
Mohad Nezhad; Gary Bending; Tim Sullivan

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.

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Fluids, Porous Media, Continuum, Engineering, Life Sciences, Mathematics Continuum

Memory matters : Beyond Markovian models of rare event kinetics

Supervisors:
David Quigley

Rare events involve rapid but infrequent transitions between two states of a system, e.g. a metastable parent liquid A and a stable crystal B. This project will extend the state-of-the-art for models of how often a system transitions from state A to state B with two key developments; (1) techniques from signal processing to fit models of collective dynamics which incorporate memory (2) machine learning of committor functions, the probability that a microstate will evolve to B before returning to A. Combined, these two developments will allow us to make enhanced predictions from atomistic simulations.

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Molecules, Atomistic, Rare Events, Physics, Chemistry

Quantum