Ph.D. Studentships: Superconductivity

Fully funded 3.5 or 4 year Ph.D. studentships are available with flexible start dates. For details see:

http://community.dur.ac.uk/superconductivity.durham/vacancies.html

Lattice Boltzmann modelling of wetting on liquid infused surfaces

Using muons to investigate low-dimensional magnetism

In some magnetic materials interactions between the moments are constrained to act along one-dimensional lines or in two dimensional planes. The physics of these systems is very different to their three-dimensional counterparts and features exotic states and excitations, including unusual forms of quantum disorder, and topological excitations such as magnetic solitons and skyrmions.

Many examples of these materials can be produced using molecular building blocks so that the interactions between the magnetic units can be adjusted and tuned. In this project we will investigate the physics of low-dimensional magnets using implanted muons, which are sensitive, microscopic magnetometers that can be produced at particle accelerators and implanted in materials.

Muons are uniquely sensitive to the low-moment magnetism in these systems and can be used to probe magnetic transitions and dynamics. The muon can also form quantum-mechanically entangled states with its surroundings whose properties, which can be accurately calculated, are of fundamental interest.

For further information, please contact Prof. Tom Lancaster (tom.lancaster@durham.ac.uk), or the Senior Postgraduate Research Administrator (physics.postgraduate@durham.ac.uk).

Learning protein dynamics

The Degiacomi group at Durham University is offering a studentship in the area of machine learning for computational biophysics.

All living organisms contain millions of proteins; biopolymers that fold into three-dimensional biologically active structures playing a vital role in the regulation of life and diseases. Research has seen a lot of focus on determining the atomic structure of different proteins. However, the flexible movement of these biopolymers plays a crucial role in their biological (mal)function.

In recent years, machine learning has been revolutionizing the way we interpret data in many scientific areas. For example, the deep neural network AlphaFold2 can predict the 3-diminsonal structures of proteins, whose shape is not known experimentally [1]. In our research, we have designed a deep neural network that can also learn an ensemble of structures of specific proteins from molecular simulations [2]. This project builds upon this breakthrough.

In collaboration with the Willcocks group (Department of Computer Science), you will develop a general neural network capable of learning and predicting the dynamics of any protein. The neural network will be trained with existing and new data you will produce from molecular dynamics simulations. Applications of this work are vast, ranging from understanding the effect of genetic mutations in cancers to informing the design of proteins to carry out a desired function.

Do you have a background in physics, computer science, chemistry, biology, or related discipline, and are keen to develop your computational skills to address biomolecular problems? Have you achieved or are you expected to get a first-class or high 2.1 honours degree?

Then please direct any informal enquiries (CV, Cover Letter, and Transcripts) to Dr Matteo Degiacomi (matteo.t.degiacomi@durham.ac.uk), Department of Physics, Durham University. Further information on the research of the Degiacomi and Willcocks groups can be found at www.degiacomi.org and cwkx.github.io. Application will be considered until the 3rd April.

[1] J. Jumper et al., Highly accurate protein structure prediction with AlphaFold, Nature 596, 2021.

[2] V.K. Ramaswamy, S.C. Musson, C.G. Willcocks, M.T. Degiacomi. Learning protein conformational space with convolutions and latent interpolations, Physical Review X 11, 2021.

For information, contact Dr Matteo Degiacomi: matteo.t.degiacomi@durham.ac.uk

Developing Highly Efficient Organic Emitters.

Molecules exist in a particular electronic distribution called the ground state. However, when they absorb energy in the form of a photon or by the action of an electric field, the ground-state electronic distribution is perturbed and a new metastable electronic arrangement called the excited state is created. Molecules are not stable in their excited state and need to release excess energy by emitting photons, or by releasing energy in the form of heat (internal conversion). Molecules are also able to store energy in the form of non-emissive triplet states, in a process called intersystem crossing. The competition between the rate of the radiative process and the rates associated with other forms of energy dissipation limit the luminescence efficiency. Molecules that are able to store the excess energy by creating large number of non-emissive triplets, or those that easily dissipate energy through vibrations (heat) are weak emitters. Remarkably, the limitations imposed by heat dissipation are transversal to all applications involving molecular systems, and seriously affects the performance of dyes and devices in many different areas.

In organic light emitting diodes (OLEDs) that convert electrical energy into light, 75% of the species that are initially created are non-emissive triplets, and if these are not harvested the efficiency of the device is limited to just 25%, but even this 25% are affected by heat dissipation resulting in much lower efficiency. Organic solar cells have their performance also limited by energy dissipation. After light is absorbed, if the rate of energy dissipation dominates, the geminate charge pair that is created upon photon absorption will end up recombining directly to the ground-state, and no photocurrent will be created. Limiting the rate of energy dissipation is thus crucial to give better chances for the charges to dissociate and to produce current. It may appear as a surprise, but strong emitters have better chances to perform well in photovoltaic devices than bad emitters do. In sensing, bioimaging and in the development of luminescent tags, a strong photoluminescence yield is obviously crucial for these dyes to perform well. Unfortunately, strategies to maximize the luminescence efficiency remain elusive and the mechanism controlling energy dissipation remains challenging and difficult to treat theoretically.

This project seeks to develop strategies to maximize the luminescence efficiency of molecular compounds, targeting in particular the low energy region of the spectrum, including the near-infrared. The aim is to understand how the radiative decay rate can be improved in the low energy region to produce emitters that are more efficient. The results obtained from this project will generate significant developments in diverse technologies that operate across the entire visible spectrum, including the NIR.

For more details, please contact Dr. Dias: f.m.b.dias@durham.ac.uk

Understanding and Controlling Spin Current Transport for Spintronics Applications

Spintronics is a critical technology for magnetic information storage including the hard-disk drive, that underpins cloud computing, and magnetic random access memory, MRAM, that has wider electronic memory applications. Spintronics describes the broad range of physics interactions between the spin component of electronic currents and the magnetization in multilayered thin-film systems. This experimental project aims to investigate the physics needed to effectively control spin current transport for the development of more energy efficient applications in spintronics. One aim is to combine metallic and semiconducting thin films.

For information, please contact Professor Del Atkinson: del.atkinson@durham.ac.uk