PhD Research Studentships
Modelling Electronic Structure and Transport in Disordered Semiconductor Alloys for Thermoelectric Applications: DFT and Machine Learning
| Supervisor: | Ricardo GRAU-CRESPO |
| Apply by: | 10 July 2025 |
| Start in: | September (Semester 1) |
Description
Thermoelectric materials can convert heat into electrical energy, offering a promising route to sustainable power generation and waste heat recovery. Their performance depends on a delicate balance between thermal and electrical transport properties. Semiconductor alloys are widely used in thermoelectric applications due to their tuneable electronic properties as a function of composition. These materials typically exhibit site-occupancy disorder, which plays a crucial but poorly understood role in determining their transport behaviour.
This PhD project will focus on the development of new theoretical and computational methods to describe and predict the effects of site disorder on the electronic structure and charge transport in thermoelectric semiconductor alloys. The student will employ ensemble-based approaches to model the statistical nature of disordered materials. Using first-principles calculations, tight-binding models, and machine learning, the project will investigate how site-occupancy disorder influences band dispersion, band broadening, and carrier scattering rates.
The aims of this PhD include:
(1) Developing and implementing novel and efficient methodologies for the prediction of carrier scattering rates, including alloy scattering, and using these to calculate transport coefficients via the Boltzmann transport equation.
(2) Applying the developed methods to case studies of disordered thermoelectric materials, such as lead chalcogenide solid solutions and disordered chalcopyrites.
The student will be based in the School of Engineering and Materials Science at Queen Mary University of London and will be supervised by Dr Ricardo Grau-Crespo. The project is computational in nature and will involve code development and the use of high-performance computing. The student will be part of a collaborative research team working on a broader programme to develop new tools for simulating disordered materials.
Eligibility
Available to applicants with UK Home Fee Status only. For details about UK Home Fee status, please visit: http://www.welfare.qmul.ac.uk/money/feestatus/
Funding
Funded by: Queen Mary ResearchUK students only.
The successful home fees status applicant will receive home tuition fees and an annual tax-free stipend set at the UKRI rate (£ 21,237 for 2024/25), UK applicants only.
Eligibility
- The minimum requirement for this studentship opportunity is a good honours degree (minimum 2(i) honours or equivalent) or MSc/MRes in a relevant discipline.
- If English is not your first language, you will require a valid English certificate equivalent to IELTS 6.5+ overall with a minimum score of minimum score of 6.0 in each of Writing, Listening, Reading and Speaking).
- Candidates are expected to start in September (Semester 1).
Contact
For informal enquiries about this opportunity, please contact Ricardo GRAU-CRESPO.
Apply
Start an application for this studentship and for entry onto the PhD Materials Science full-time programme (Semester 1 / September start):
Please be sure to quote the reference "SEMS-PHD-668" to associate your application with this studentship opportunity.
| Keywords: | Computational Chemistry, Inorganic Chemistry, Artificial Intelligence, Machine Learning, Energy Technologies, Materials Science - Other, Chemical Physics, Computational Physics, Solid State Physics |