AI-Enabled Atomistic Modelling of Dynamic Interfaces in Alcohol Electro-Oxidation

A key challenge in understanding these reactions and achieving high catalytic performance is the dynamic nature of the catalyst–electrolyte interface. Under operating conditions, catalyst surfaces reconstruct and the surrounding solvent and electrolyte environments reorganize, directly affecting reaction pathways, proton transfer, and product selectivity. These processes are difficult to capture using conventional first-principles simulations alone. Recent advances in artificial intelligence (AI)-enabled atomistic modelling, particularly machine-learning interatomic potentials, provide a powerful means of accessing the relevant configurational and temporal scales.

This PhD project will investigate how catalyst reconstruction and interfacial proton transfer govern alcohol electro-oxidation. Nickel-based (oxy)hydroxides will be used as a representative model system to explore the structural evolution of the catalyst, the competition between direct and reconstructed-phase-mediated oxidation pathways, and the role of interfacial proton acceptors.

The project will combine density functional theory, molecular simulation, and machine-learning interatomic potentials, in close integration with electrochemical experiments and in situ or operando characterization. The broader goal is to establish a molecular-level understanding of how evolving catalyst structures control reaction mechanisms under electrochemical conditions.

Candidate Profile

Applicants should have a strong background in chemistry, materials science, chemical engineering, physics, or a related discipline. Experience in electrochemistry, catalysis, computational chemistry, DFT, molecular dynamics, Python, machine learning, or high-performance computing would be advantageous.

Prior experience with machine-learning interatomic potentials is desirable but not required. The project is particularly suitable for candidates interested in AI for chemistry, computational electrocatalysis, and fundamental reaction mechanisms.