Digital Routes to Next Generation Solid Oxide Electrolysis Cells

Reference number: CCPR125


A key technology in the drive towards net-zero emissions is the production of green hydrogen by electrolysis powered by renewable energy. The solid oxide electrolysers of Ceres Power are world leading in efficiency and reliability. To maintain this position, new materials are required to improve performance and sustainability across all the components of the devices: new electrodes with improved sustainability, and new solid electrolyte compositions with improved mechanical and electronic properties.

This project will develop an automated computational workflow for the discovery of new electrolyte and electrode materials for solid oxide cells. The workflow will combine crystal structure prediction for composition stability determination and computational modelling of key properties including oxide conductivity and mechanical stability. This physical modelling will be supported by machine learning from databases already available to the project team and from the arising modelling data, extending to the use of large language models. Machine learning techniques such as supervised learning and semi-supervised learning will potentially be employed to learn complex representations from both labelled and unlabelled data and to predict material properties. Generative models, such as generative adversarial networks and diffusion models, will potentially be used for generating new material compositions with optimised properties. The student will have the opportunity to synthesise and evaluate the new materials as well as developing computational skills, thus developing a broad expertise base. They will work with an interdisciplinary team at Liverpool and Ceres Power to maximise the impact of the project. This new approach builds on recently established capability from the team [1,2] applied in this exciting new direction for net zero technologies.

The student recruited to this project will also be part of a wider cohort-training programme focused on the application of digital methods (data and physics based, robotics and automation) to materials chemistry and will be based in the Materials Innovation Factory at Liverpool.

Please apply by completing the online postgraduate research application form here: How to apply for a PhD - University of Liverpool.

Please ensure you include the project title and reference number CCPR125 when applying.

Applicants are advised to apply as soon as possible with applications considered when received and no later than 30/06/2024. A 2i or higher degree or Masters in chemistry, physics, engineering, materials science or computer science is required.

We want all of our staff and Students to feel that Liverpool is an inclusive and welcoming environment that actively celebrates and encourages diversity. We are committed to working with students to make all reasonable project adaptations including supporting those with caring responsibilities, disabilities or other personal circumstances. For example, if you have a disability you may be entitled to a Disabled Students Allowance on top of your studentship to help cover the costs of any additional support that a person studying for a doctorate might need as a result.


Open to students worldwide

Funding information

Funded studentship

The EPSRC funded Studentship will cover full tuition fees of £4,786 per year and pay a maintenance grant for 4 years, starting at the UKRI minimum of £19,237 pa. for 2024-2025. The Studentship also comes with access to additional funding in the form of a research training support grant which is available to fund conference attendance, fieldwork, internships etc.

EPSRC Studentships are available to any prospective student wishing to apply including international students. Up to 30% of our cohort can comprise of international students and they will not be charged the fee difference between UK and international rate.



  1. Vasylenko, et al., Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties, npj Comput. Mater.2023,9, 164
  2. Hargeaves, et al., A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning, npj Comput. Mater., 2023, 9, 9