Machine Learning approaches to whole-particle modelling of Pt nanoparticles for fuel cells

The goal is to build a new predictive framework for designing platinum-alloy catalysts for hydrogen fuel cells. Specifically, the project aims to: (i) develop machine-learned interatomic potentials trained on large-scale DFT data; (ii) apply automated reaction discovery to identify key steps in the oxygen-reduction reaction on realistic nanoparticle models; and (iii) quantify uncertainties in predicted reaction pathways and compare to experimental data from our partners. Together, these advances will enable faster, more accurate exploration of composition and structure space, directly supporting industrial efforts to reduce platinum use while improving catalyst efficiency and durability in electrochemical energy systems.

By the end of the PhD, the student will have produced a validated multiscale modelling workflow for modelling whole Pt-based alloy nanoparticles, and studying the catalytic reactions that take place on their surfaces. The work will generate mechanistic insight into how structure, composition, and electrochemical bias affect reaction kinetics, and will identify promising lowplatinum catalyst candidates. Tangible outcomes will include publishable results in high-impact journals, open-source software improving LS-DFT, machinelearning potential, and reaction-discovery codes, and datasets suitable for industrial and academic use. The resulting tools will form a foundation for future research on fuel-cell catalysis and related electrochemical systems.

The student will develop expertise at the interface of physics, chemistry, and data science. Core technical skills will include large-scale density functional theory, machine-learned interatomic potentials, automated reaction-discovery algorithms, and uncertainty quantification. They will gain experience in software development and high-performance computing, contributing directly to open-source research codes such as ONETEP, MACE, and Kinetica.jl. Through collaboration with industrial and academic partners, they will also build transferable skills in interdisciplinary teamwork, data analysis, and scientific communication—preparing them for research or applied careers in computational materials science, catalysis, or sustainable energy technology.

These skills position you for careers in AI research, computational materials science, national laboratories, tech industry or academic research. The HetSys training provides a foundation for these skills through dedicated courses and cohort activities.

• Exploring fuel cell cathode materials using ab initio high throughput calculations and validation using carbon supported Pt alloy catalysts, M. Sarwar, J. L. Gavartin, A. Martinez Bonastre, S. Garcia Lopez, D. Thompsett, S. C. Ball, A. Krzystala, G. Goldbeck and S. A. French, Phys. Chem. Chem. Phys. 22, 5902-5914 (2020).
• Predicting the Oxygen-Binding Properties of Platinum Nanoparticle Ensembles by Combining High-Precision Electron Microscopy and Density Functional Theory, J. Aarons, L. Jones, A. Varambhia, K.E. MacArthur, D. Ozkaya, M. Sarwar, C.-K. Skylaris, P. D. Nellist, Nano Lett. 17, 7, 4003 (2017).
• Predicting long-time-scale kinetics under variable experimental conditions with Kinetica. jl, J Gilkes, MT Storr, RJ Maurer, S Habershon, Journal of Chemical Theory and Computation 20 (12), 5196-214 (2024).
• Graph-driven reaction discovery: progress, challenges, and future opportunities, I Ismail, R Chantreau Majerus, S Habershon, The Journal of Physical Chemistry A 126 (40), 7051-7069 (2022).
• Successes and challenges in using machine-learned activation energies in kinetic simulations, I Ismail, C Robertson, S Habershon, The Journal of Chemical Physics 157 014109 (2022).

We require at least a II(i) honours degree at BSc or an integrated masters degree (e.g. MPhys, MChem, MSci, MEng etc.) in a physical sciences, mathematics or engineering discipline. We do not accept applications from existing PhD holders.

If you are an overseas candidate please check here that you hold the equivalent grades before applying.

For postgraduate study at Warwick, the term “overseas” or “international” student refers to anyone who does not qualify for UK home fee status. This includes applicants from the European Union (EU), European Economic Area (EEA), and Switzerland, unless they hold settled or pre-settled status under the UK’s EU Settlement Scheme.