A fully-funded 4-year PhD studentship in theoretical / computational chemistry is available in the Department of Chemistry at the University of Bath. The project is supervised by Dr Benjamin Morgan and Prof Saiful Islam, and is suitable for students with an interest in solid state computational chemistry, materials science, programming and software development, and / or machine learning and data analysis.
The global transition to low-carbon and renewable energy sources requires improvements in clean energy storage technologies such as rechargeable batteries. Lithium-ion batteries have been hugely successful, but require further improvements if technologies such as electric vehicles are to become widespread. One limitation of today’s lithium-ion technologies is the use of organic liquid electrolytes, which are electrochemically unstable at high voltage and are flammable, presenting safety issues. Replacing these liquid electrolytes with ion-conducting solid electrolytes is one possible route to improved batteries that can use high voltage electrodes and offer higher energy densities, while being safer to operate.
To develop high performance solid electrolytes, it is important to understand how changing the chemical composition and structure of these materials affects their ability to allow fast lithium-ion conduction. Computer simulation is a powerful tool for studying lithium-ion diffusion because it provides an atomic scale description of the motions of individual atoms. The continuing increase in available computing power makes it possible to run simulations that are larger and more accurate than ever before. As the volume of data from these simulations increases, analysing this to extract fundamental descriptions of lithium diffusion becomes increasingly challenging. This growing volume of data also provides a new opportunity: by applying modern statistical and data analysis methods can we extract quantitative information about the lithium diffusion mechanisms using automated procedures?
This project aims to answer this question by developing and new computational data-analysis techniques for processing simulation data for lithium-ion solid electrolytes. The project will also consist of performing computer simulations of a range of so-called "superionic" solid electrolytes—which have ionic conductivities comparable to commercial liquid electrolytes—before analysing the resulting data to understand how the chemistry and structure of these materials together give them such unusually high ionic conductivities. This insight will allow us to develop "design rules" for new, improved, solid electrolytes, which will be synthesised and tested by our experimental partners.
This project will run in collaboration with the group of Dr. Wolfgang Zeier at the University of Giessen, Germany, who have vast experience in the synthesis and characterisation of solid electrolytes. The project will also interact with researchers from the Computer Science department at the University of Bath, to provide support in applying modern machine learning techniques in solving this problem.
Prospective students with an interest in computational chemistry, programming and software development, and machine learning and data analysis are encouraged to apply.
Applicants should hold, or expect to receive, a First Class or high Upper Second Class UK Honours degree (or the equivalent qualification gained outside the UK) in a relevant subject. A master’s level qualification would also be advantageous.
Informal enquiries should be directed to Dr Ben Morgan, email [email protected]
Formal applications should be made via the University of Bath’s online application form: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCH-FP01&code2=0014
Please ensure that you quote the supervisor’s name and project title in the ‘Your research interests’ section.
More information about applying for a PhD at Bath may be found here: http://www.bath.ac.uk/guides/how-to-apply-for-doctoral-study/
Anticipated start date: 20 January 2020.
Later start dates will be considered; however, applicants must be available to start prior to the end of March 2020.