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The catalytic conversion of CO into methanol is a central route for turning an abundant carbon source into a liquid energy carrier and chemical intermediate. At the atomic scale, this reaction depends on a delicate balance between CO activation, hydrogen transfer, stabilization of C1 oxygenated intermediates and desorption of methanol or competing products. Small changes in the local geometry or electronic structure of the active site can therefore redirect the reaction pathway, which makes surface-level understanding essential for rational catalyst design.

Single-atom catalysts supported by graphene or N-doped graphene offer a controlled platform for this problem. An isolated transition-metal atom coordinated in a carbon/nitrogen environment can provide a well-defined active site while maximizing metal efficiency. However, the relevant chemical space remains large: the metal identity, coordination motif, nearby defects, hydrogen coverage and reaction intermediates all modify activity and selectivity. Density functional theory can describe these effects accurately, but systematic exploration of many sites and elementary steps rapidly becomes computationally expensive. Conversely, general-purpose machine-learning potentials are not yet sufficiently reliable for rare reactive configurations at surfaces. The central idea of this PhD project is to build a focused, data-efficient modeling strategy for this specific class of reactive interfaces.

PhD project

The thesis will develop a computational workflow to study CO hydrogenation on graphene-supported single-atom catalysts, with emphasis on transition-metal sites coordinated by carbon and nitrogen motifs. The first step will be to establish a robust DFT reference for representative adsorption states and elementary reaction steps, including CO bending/activation, hydrogen addition, formate or carboxyl-like intermediates, methoxy formation and methanol release. This reference will be used to identify the structural motifs and reaction coordinates that control activity and selectivity.

The methodological core of the work will be the construction of an informative training set rather than the accumulation of a large, redundant database. Candidate configurations will be generated from DFT relaxations, reaction-path searches, molecular dynamics snapshots and targeted distortions around key intermediates. They will then be filtered using structural diversity, physical consistency and uncertainty indicators, so that expensive reference calculations are concentrated on configurations that genuinely improve the model. Based on this curated data, the PhD student will train and test machine-learning force fields or Δ-learning corrections able to reproduce energies, forces and selected reaction barriers in the relevant domain. The aim is to obtain models that are useful for catalyst screening because their limits are explicitly diagnosed, not simply because they perform well on equilibrium structures.

Scientifically, the project will address three linked questions: which metal-coordination environments activate CO without trapping intermediates too strongly; which elementary steps are most sensitive to the local structure of the single-atom site; and how far active data selection can reduce the cost of atomistic modelling while preserving quantitative accuracy on reaction energetics. The project naturally builds on previous IJL work on alloy surfaces, adsorption and CO hydrogenation, and on recent developments in machine-learning potentials for surfaces and reactive interfaces.

AApplicant skills: Strong background in chemistry, physical chemistry, materials science, or condensed matter physics. Experience in data science, Python programming, high-performance computing and/or quantum chemistry will be considered an asset. Excellent communication skills are essential, with the ability to work and exchange ideas effectively both orally and in writing. English speaking is required. The application should include a statement of research interest, a CV and Master's degree transcript.