PhD Studentship - Deep Learning-based Mechanistic Elucidation of Processes Mediated by Heterogeneous Catalysts

Application deadline: All year round

Research theme: Catalysis and artificial intelligence

How to apply: uom.link/pgr-apply-2425

How many positions: 1

This 4 year PhD project is funded by an EPSRC Industrial Doctorate Landscape Award supported by BP. The award will cover tuition fees and an annual tax free (depending on circumstances) stipend will be provided based on the UKRI rate (£19,237 for 2024/25). We expect the stipend to increase each year. The project will start in October 2025.

Heterogeneous catalysis is essential in the chemical industry, but understanding its mechanisms is complex due to the limitations of traditional kinetic analysis, which often relies on simplifying approximations. Recent advances in the Larrosa and Bures groups have led to a machine learning approach that predicts reaction mechanisms directly from experimental kinetic data, without such approximations. This AI model has demonstrated success in identifying key mechanistic pathways, including catalyst activation and deactivation, across various homogeneous catalytic processes, offering a more accurate and broadly applicable tool for mechanistic discovery. 

In this project, we will create new machine learning-based tools to process and analyse kinetic data of heterogeneous catalytic systems, and predict their corresponding reaction mechanism, including deactivation pathways. This will be a multipurpose tool capable of tackling a wide range of heterogeneous catalytic processes with a single training. We will use these models to facilitate the design of experiments to maximize mechanistic insights from the least number of experiments with a focus on current processes of interest within bp, such as the Fischer-Tropsch process (cobalt and iron), alcohol dehydration, olefin oligomerisation, and methanol synthesis.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline, such as Chemistry, or Chemical Engineering.

Please contact the main supervisor; Dr. Igor Larrosa: igor.larrosa@manchester.ac.uk before you apply. 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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