We are advertising for several PhD opportunities in the Prentice Group, with flexible start dates, in the areas of computational method development and materials for quantum technology. Details of these projects are listed below, and can also be found on FindAPhD.
These projects have no specific funding attached to them, but there are many funding opportunities available for PhDs at Manchester, for both home and overseas students (see Join us for more details). We particularly encourage applications from those who identify as belonging to a group under-represented in science and engineering; there are also more funding pathways available for such applicants. Self-funded applications are also very welcome.
All applications should be made through the University of Manchester online system. If you’re interested in one of these projects, please do get in touch with Joe!
Enabling high-accuracy first principles modelling of nanoscale systems: many-body quantum embedding in linear-scaling DFT
Density functional theory (DFT) has been the most popular method for understanding the electronic structure of materials from first principles for over 30 years, thanks to its balance of computational efficiency, accuracy, and simplicity. Well-developed derivatives of DFT, such as time-dependent DFT (TDDFT), allow for the calculation of response properties, such as the excitation of electrons by the absorption of light.
However, standard (TD)DFT has several well-known systematic issues that limit its utility. The cost of standard DFT scales cubically with the number of electrons, restricting calculations to relatively small systems; linear-scaling DFT has been developed as a way to reduce this scaling and model larger, nanoscale, systems. DFT also often underestimates band gaps, is inaccurate in its treatment of excitons, and is ineffective in systems with strong electron correlation. More advanced techniques are able to address these issues, such as the GW approximation, the Bethe-Salpeter equation (BSE), and wavefunction-based methods, but these often come at a much higher computational cost. This means that, currently, it is very difficult to apply these high-level methods to the complex large-scale materials relevant in many applications, including quantum technology, photovoltaics, and biochemistry.
This project is therefore focused on developing new methods to bring these higher-level techniques to bear on complex systems, by combining them with linear-scaling DFT. The main approach used for this will be quantum embedding, where only a small portion of the system is treated at the high level of theory, with the rest treated at a lower level, maintaining self-consistency throughout. The initial focus would be on embedding GW and BSE methods within linear-scaling DFT, significantly increasing the accuracy of excited state calculations available for complex systems. In the longer term, the project would encompass even higher-level methods, such as wavefunction-based methods. These cutting-edge techniques would be implemented primarily in the ONETEP code, which is developed in the group. To test these newly developed techniques, the project would also involve simulating systems with practical applications, such as defects for quantum technology, or organic photovoltaics.
This project will suit a student with an interest in computational modelling and code development, and a background in physics, chemistry, materials science, or related disciplines. Experience in coding, particularly in Python and FORTRAN, would be beneficial, but not necessary.
Searching for novel defects for quantum technology applications with first principles modelling
The success and widespread use of the next generation of quantum technology will fundamentally depend on the materials used to make them. One of the leading classes of materials for these applications is colour centres in crystalline semiconductors (such as the well-known nitrogen-vacancy centre in diamond), thanks to their stability, long spin coherence lifetimes, and ability to operate near room temperature. Despite significant advances in the last few years, there are still fabrication challenges associated with the leading candidate systems of this class, as well as issues to overcome around environmental interactions. There is therefore still intense research interest in identifying new candidate colour centres for quantum technology applications, with the aim of improving on the state-of-the-art.
First principles modelling is a vital tool in this endeavour, as it allows us to explore the vast range of possible colour centres efficiently. This includes establishing which defect complexes are energetically favourable, how mobile they are, their excited state properties, and the influence environmental effects can have on these properties. With this information in hand, we can computationally screen candidate systems, using theory to guide experiment and industry towards new materials.
In this PhD project, we will make use of cutting-edge computational methods, including linear-scaling time-dependent density functional theory (LS-TDDFT), quantum embedding, and machine learning potentials, to systematically explore this vast space for novel candidate defects for quantum technology applications. The initial focus will be on complexes formed around implanted ions from across the periodic table, particularly focusing on Group IV, Group V, and the transition metals. This will initially be focused on diamond as a host material, but will expand out to explore candidates in host materials such as silicon, SiC, GaN, and others. The simulations performed within the project will provide insight into the fundamental physics of these systems, their robustness against external perturbations, and their suitability for how best to fabricate them. An important aspect of this work will be comparing results and predictions against experimental data where available; there will also be the opportunity to collaborate with world-leading experimental groups in this area.
If the student is interested, there will also be scope within the project for developing more powerful computational modelling methods for describing complex systems at a quantum mechanical level, based on quantum embedding. This development work would be done alongside other members of the group.
This project will suit a student with an interest in computational modelling and/or materials for quantum technology, and a background in physics, chemistry, materials science, or related disciplines. An interest in code development, especially for materials modelling software, would be beneficial, but not necessary.
Understanding the influence of extended defects and surfaces on defects for quantum technology applications with first principles modelling
The success and widespread use of the next generation of quantum technology will fundamentally depend on the materials used to make them. In particular, the way these materials interact with light drives many quantum technology applications, from quantum computing to sensing to communications. As these technologies move towards commercialisation, it is vital that the most relevant properties are preserved as the materials leave the laboratory and enter the real world, where a multitude of external factors will have an influence. One of these is the presence of crystalline defects, including extended defects such as dislocations; another is the influence of interfaces, as these defects are often fabricated close to the surface.
Disentangling and controlling the individual influence of these factors on the relevant light-matter interactions experimentally can be difficult. A compelling alternative is therefore to simulate the interaction of light with these materials from first principles (i.e., directly from the equations of quantum mechanics). In simulations, we can control these external factors much more easily, getting a better understanding of the physics in play. For these complex systems, however, gaining this computational insight can require simulating several thousand atoms to high levels of accuracy, which is challenging with existing computational methods.
In this PhD project, we will make use of cutting edge computational methods, including linear-scaling time-dependent density functional theory (LS-TDDFT), quantum embedding, and machine learning potentials, to drive a step-change in the understanding of these environmental effects. The project will focus on a particular class of materials for quantum technology – that of colour centres in crystalline semiconductors, the most well-known being the nitrogen-vacancy centre in diamond – and consider the influence of extended defects and surface effects on the excited state properties of the system. The project will aim to explore several different contenders for quantum technology applications, providing insight into the fundamental physics of these systems, their robustness against these environmental influences, and how best to fabricate them. An important aspect of this work will be comparing results and predictions against experimental data where available; there will also be the opportunity to collaborate with world-leading experimental groups in this area.
If the student is interested, there will also be scope within the project for developing more powerful computational modelling methods for describing complex systems at a quantum mechanical level, based on quantum embedding. This development work would be done alongside other members of the group.
This project will suit a student with an interest in computational modelling and/or materials for quantum technology, and a background in physics, chemistry, materials science, or related disciplines. An interest in code development, especially for materials modelling software, would be beneficial, but not necessary.
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