A fully funded PhD studentship in the Department of Mechanical Engineering
Accelerated prediction of surface reactions in catalysts for H generation using Machine Learning and coarse-graining methods
Lead supervisors: Dr Enrique Galindo-Nava
Application deadline: Ongoing
Project start date: 01 October 2024
Project duration: 4 years
Eligibility: Open to UK students and EU students with settled or pre-settled status in the UK
Studentship funding: Home tuition fees (currently £5,860/year) and maintenance stipend (currently £23,622/year)
Project location: UCL Bloomsbury campus
PhD project description
Water electrolysis is regarded as the most viable solution to produce clean hydrogen but electrode materials used in commercial technologies face several challenges risking the mass production of hydrogen. Key issues include poor catalyst stability under harsh operating conditions and high cost of raw materials. Central to address these problems is delivering fundamental understanding of the kinetic processes occurring at the surface of catalysts, which govern material efficiency and stability. Atomistic modelling offers the possibility to address the above-mentioned challenges, but more work is needed to define multi-scale models that can predict the long-term kinetics associated to material degradation and which existing methods cannot tackle.
This project will develop a (coarse-grained) self-learning kinetic Monte Carlo model -supported by various computational methods- to study main stability mechanisms, including phase transformations, material leaching, etc. and propose material-related enhancements for improved electrolyser performance. Ni-based oxides used in alkaline electrolysers will be considered initially in the study.
Activities in the project will include:
- Develop a machine-learning based interatomic potential of Ni-based oxides combining active learning models and ab-initio calculations to establish the Thermodynamics aspects of material stability.
- Develop a self-learning kinetic Monte Carlo algorithm to predict the Kinetics of material degradation and identify pivotal material properties (e.g. alloy content, structure, defects, etc) dictating their efficiency.
- Combine the results to study surface activity and mass transport under different material and operating conditions to propose and validate material changes for enhanced stability.
The studentship is sponsored by BP plc, via an iCASE award, and we expect the student to be actively engaged with their research team and other project partners. We offer a unique opportunity to collaborate with a highly interdisciplinary team of academics and industrialists using state-of-the-art computational techniques to help realising the Hydrogen economy. The successful candidate will be encouraged to attend national and international conferences and publish high-impact papers to disseminate the outcomes of the project.
Applicants should have (or expected to be awarded) an upper second or first class UK honours degree at the level of MSci or MEng (or overseas equivalents) in a relevant Physics, Engineering or Science subject, including Materials Science, Engineering, Physics, Chemistry, Applied Mathematics or related disciplines.
Due to funding restrictions the studentship is only open to candidates who qualify for Home tuition fees: candidates from the UK or from the EU with settled or pre-settled status in the UK. Please refer to our website for further information about Home tuition fee eligibility.
Applicants whose first language is not English are required to meet UCL's English language entry requirements.
Please refer to this webpage for full eligibility criteria: Mechanical Engineering MPhil/PhD
How to apply
Eligible applicants should first contact Dr Enrique Galindo-Nava (firstname.lastname@example.org). Please enclose the following documents:
- A one-page statement outlining suitability for the project
- A two pages CV (including contact details of two referees)
After discussing the project with Dr Galindo-Nava, eligible applicants should also submit a formal PhD application via the UCL website.
The supervisory team will arrange interviews for short-listed candidates.