Solution synthesis of multi-anion functional materials
- Supervisors: Prof M J Rosseinsky Dr Luke Daniels
Description
Solution synthesis routes to functional materials offer opportunities to new crystal structures and low temperature conditions not possible through sub-solidus solid state reactions. This project will explore solution synthesis of materials containing multiple anions for functions such as solar absorption or ionic conductivity that are central to net zero technologies. The selection of experimental targets will be informed by artificial intelligence and computational assessment of candidates, or by attempts to synthesise materials typically prepared through solid state routes. The resulting materials will be experimentally studied to assess their suitability in a wide range of applications, combining our broad materials characterisation expertise with that of our international industrial and academic collaborators. The student will thus both develop a strong materials synthesis, structural characterisation and measurement skillset, and the ability to work with colleagues across disciplines in a research team using state-of-the-art materials design methodology.
The project is based in the Materials Innovation Factory (https://www.liverpool.ac.uk/materials-innovation-factory/) at the University of Liverpool, a state-of-the-art facility for the digital and automated design and discovery of materials. The project will make use of tools developed in the multi-disciplinary EPSRC Programme Grant: “Digital Navigation of Chemical Space for Function” and the Leverhulme Research Centre for Functional Materials Design, that seek to develop a new approach to materials design and discovery, exploiting machine learning and symbolic artificial intelligence, demonstrated by the realisation of new functional inorganic materials. Examples include the first tools to guarantee the correct prediction of a crystal structure (Nature 68, 619, 2023), and to learn the entirety of known crystalline inorganic materials and guide discovery (Nature Communications 12, 5561, 2021).
As well as obtaining knowledge and experience in materials synthesis, crystallography and measurement techniques, the student will develop skills in teamwork and scientific communication, as computational and experimental researchers within the team work closely together. There are extensive opportunities to use synchrotron X-ray and neutron scattering facilities.
Applications are welcomed from students with a 2:1 or higher master’s degree or equivalent in Chemistry, Physics, or Materials Science, particularly those with some of the skills directly relevant to the project outlined above. Experience in air sensitive (Schlenk) techniques, structural characterisation of inorganic materials or electron microscopy is an advantage.
This position will remain open until a suitable candidate has been found.
Applications from candidates meeting the eligibility requirements of the EPSRC are welcome – please refer to the EPSRC website http://www.epsrc.ac.uk/skills/students/help/eligibility/.
The studentship is open to Home, EU and international students, however, please be aware there is a limit on the number of international students we can appoint per year. Further the studentship does not cover international fees.
Please ensure you include the project title and reference number CCPR136 for when applying.
Availability
Open to UK applicants
Funding information
Funded studentship
Funding will cover full home (UK national) tuition fees and a stipend set at the UKRI rate for a period of 3.5 years. The stipend amount for students starting in the 2024/2025 academic year is £19,237 and will rise slightly each year with inflation.
The funding for this studentship also comes with a budget for research and training expenses of £1000 per year, and for those that are eligible, a disabled students allowance to cover the costs of any additional support that is required.
Supervisors
References
[1] V. V. Gusev et al., Optimality guarantees for crystal structure prediction, Nature 619, 68-72, (2023).[2] A. Vasylenko et al., Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry, Nature Commun. 12, 5561, (2021).