Machine-learned modelling of supported cobalt nanoparticles under Fischer-Tropsch conditions
Fischer-Tropsch synthesis (FTS) is a key technology for converting syngas into liquid hydrocarbons and can support a transition to renewable fuels when the hydrogen is produced from renewable electricity and the carbon feed (CO/CO2) is sourced from point sources. Cobalt catalysts are central to industrial FTS, yet their active structure is dynamic: cobalt nanoparticles supported on refractory oxides can restructure, oxidize, or carburize depending on the local chemical potential of oxygen and carbon under operating and pretreatment conditions.
Understanding how supported nanoparticles transform is typically approached with plane-wave density functional theory (DFT), but the relevant length- and time-scales (particle sizes, multiple motifs, surface reconstructions, and environment-driven changes) make exhaustive exploration prohibitively expensive at the MSc level. In this project, you will use a MACE machine-learned interatomic potential as an efficient, physics-informed alternative to sample nanoparticle structures and transformations on a refractory oxide support. The goal is to map how cobalt particle morphology and chemical state evolve as a function of process conditions representative of renewable FTS, including strongly oxidative (CO2/H2O-rich) and carburizing (CO-rich) environments.
Figure 1: Cobalt nanoparticle on an alumina under the influence of a strong carburizing potential.
Project outline
- Review cobalt-catalyzed FTS in a renewable context: H2 from renewable resources and CO/CO2 from point sources, and identify key catalyst environments (reduction, reaction, regeneration).
- Build atomistic models of cobalt nanoparticles supported on a refractory oxide surface (e.g., alumina, silica, titania, or zirconia) and define a set of particle sizes/shapes.
- Set up and validate a MACE force field workflow for supported nanoparticles (basic sanity checks against small-scale reference calculations or known structural trends).
- Explore nanoparticle restructuring using efficient sampling (relaxations, annealing, and/or molecular dynamics) to identify stable motifs on the support.
- Investigate environment-driven transformations by modelling oxidation and carburization trends as a function of imposed conditions (e.g., oxygen and carbon chemical potential proxies, or controlled addition of O/C species) and track structural descriptors.
- Summarize how particle shape, wetting/contact, surface termination, and chemical state change across oxidative vs carburizing regimes relevant to renewable FTS feeds.
Expected outcome
A MSc-level final report containing a curated set of supported cobalt nanoparticle structures, an analysis of how morphology and interfacial structure evolve under different simulated environments, and a discussion of oxidation/carburization tendencies under conditions motivated by renewable FTS (renewable H2 combined with CO/CO2 from point sources). The report should include clear structural descriptors (e.g., particle size/shape metrics, coordination analysis, wetting/contact angles, surface composition indicators) and a critical assessment of what MACE enables compared to plane-wave DFT, including accuracy/transferability and remaining uncertainties.