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Zhichuan Jason Xu

4 days ago

PhD in AI-Enabled Atomistic Modelling of Dynamic Interfaces in Alcohol Electro-Oxidation Nanyang Technological University in Singapore

Degree Level

PhD

Field of study

Computer Science

Funding

Funded PhD project for students worldwide. Funding details beyond funded status are not specified in the post.

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Country

Singapore

University

Nanyang Technological University

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Keywords

Computer Science
Chemistry
Chemical Engineering
Materials Science
Catalysis
Artificial Intelligence
Electrochemistry
Computational Chemistry
Molecular Dynamics
Density Functional Theory
Electrocatalysis
Physics
ML

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About this position

PhD opportunity at Nanyang Technological University (Singapore) in AI-enabled atomistic modelling of dynamic interfaces in alcohol electro-oxidation.

This funded PhD project, supervised by Prof Zhichuan Jason Xu in the School of Materials Science and Engineering, focuses on understanding how catalyst reconstruction and interfacial proton transfer control alcohol electro-oxidation. The research is centered on nickel-based (oxy)hydroxides and aims to reveal how evolving catalyst structures influence reaction pathways, proton transfer, and product selectivity.

The project combines density functional theory (DFT), molecular simulation, and machine-learning interatomic potentials, integrated with electrochemical experiments and in situ/operando characterization. It is well suited to candidates interested in AI for chemistry, computational electrocatalysis, catalysis, materials science, and fundamental reaction mechanisms.

Eligibility: applicants should have a strong background in chemistry, materials science, chemical engineering, physics, or a related discipline. Helpful experience includes electrochemistry, catalysis, computational chemistry, DFT, molecular dynamics, Python, machine learning, or high-performance computing. Prior experience with machine-learning interatomic potentials is desirable but not required.

Funding: the project is advertised as a funded PhD project for students worldwide.

Applications: applications are accepted all year round. Interested candidates should register their interest via the FindAPhD enquiry form on the project page and wait for the university to respond directly.

Funding details

Funded PhD project for students worldwide. Funding details beyond funded status are not specified in the post.

What's required

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.

How to apply

Register your interest through the FindAPhD enquiry form on the project page. Submit your name, email, and any questions; the university will respond directly.

More information can be found here

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