PhD in Data-Driven Molecular Discovery for Energy Storage

The PhD project focuses on the computational discovery and optimisation of organic redox-active molecules for next-generation aqueous redox flow battery and electrochemical booster systems. Aqueous redox flow batteries are promising candidates for long duration energy storage, but their widespread deployment is limited by challenges in energy density, stability, solubility, and cost of electroactive redox compounds. The PhD candidate will develop and apply machine-learning (ML)-driven and physics-based computational workflows to screen large molecular libraries, predict key electrochemical and physicochemical properties, and deliver ranked shortlists of high-performance, cost-effective energy storage molecules and mediators. The work includes molecular property prediction, stability assessment, and matching candidate molecules to relevant electrochemical operating windows and electrolyte environments. This PhD is part of the national Redox Blend consortium, providing computational insights that directly guide experimental synthesis and validation across partner institutions.

You will be embedded in the AMD research group at DIFFER and work closely with external experimental collaborators to ensure alignment between computational models, data quality, and experimental conditions.

• Develop and extend ML and physics-based workflows, such as RedCat, for automated molecular screening for energy storage in aqueous redox flow batteries.
• Perform high-throughput DFT and MD calculations to validate and refine top-ranked molecular candidates.
• Deliver ranked shortlists and detailed property reports to guide experimental synthesis and testing.
• Work closely with project collaborators to align computational model development and data availability with evolving experimental measurement workflows.
• Disseminate research findings through publications, conference presentations, and consortium meetings.
• Supervise junior student projects, where appropriate.
• Complete and defend a PhD thesis within four years.