PhD Studentship - Theory-Led Compositional Optimisation of High-Entropy Metal Oxides for Electrocatalytic Hydrogen Generation

Research theme: Computational Electrocatalysis

How to apply: uom.link/pgr-apply-2425

This 3.5-year PhD project is fully funded, and home students are eligible to apply. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26) and tuition fees will be paid. We expect the stipend to increase each year. The start date is October 2026.

We recommend that you apply early as the advert may be removed before the deadline.

Efficient H2 production by electrolysis uses electrocatalysts to reduce reaction overpotentials and lower the energy input required. The anodic oxygen evolution reaction (OER) remains an outstanding barrier to efficient H2 production, and even with the best electrocatalysts still needs significant energy input to overcome its intrinsically high overpotential. The most effective OER catalyst, especially under acidic conditions, is iridium oxide (IrO2). However, iridium is prohibitively expensive, making electrolysers too costly for scale-up and finding inexpensive alternatives is therefore critical for sustainable H2 production.

Recently, alloys with ≥5 constituent elements, termed “high-entropy alloys” (HEAs), have shown overpotentials and stability competitive with Ir-based catalysts. The large configurational entropy, synergistic enhancement of surface energetics, and tuneable catalytic sites, allow for improved stability and performance. HEAs made from cheap transition metals have huge potential to exhibit comparable activities to rare-earth metals whilst simultaneously avoiding the stability issues normally associated with these more abundant metals. However, millions of possible alloy compositions can be formed from just five elements, making empirical composition optimisation impossibly slow.

This project will tackle this challenge head on by combining quantum-mechanical calculations with state-of-the-art machine learning (ML) methodologies to explore and optimise the compositional space of complex high-entropy metal oxides (HEMOs). Ultimately, we seek to develop approaches to effectively screen low cost, stable, and catalytically active electrocatalysts with performance competitive with industry-standard materials such as IrO2.

We are looking for a highly motivated independent learner with good coding skills to work with us at the interface of AI and electrochemistry. As a PhD researcher, you’ll work at the forefront of materials science, combining ab initio density-functional theory (DFT) calculations with novel ML methods. You will develop ML-assisted computational screening methods, building highly accurate models to accelerate the prediction of electrocatalytic properties across complex compositional spaces.

The student will work in an interdisciplinary environment in the groups of Dr Jack Swallow and Dr Jonathan Skelton in the Department of Chemistry, University of Manchester. They will make use of national and local high-performance computing facilities to carry out high-throughput adsorption energy calculations from compositional permutations of HEMOs. Computational and theoretical methodologies represent one of the major research themes in the Department of Chemistry, allowing the student to learn from leading experts in the field. The project will also link strongly to experimental electrochemistry research in the Henry-Royce institute, with over £150m of related equipment, with opportunities to collaborate on the synthesis and validation of compositions identified by the modelling.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or Master’s (or international equivalent) in Chemistry, Materials Science, Physics, or a relevant science or engineering related discipline.

To apply, and for more information, please contact the main supervisor; Dr Jack Swallow - jack.swallow@materials.ox.ac.uk. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

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