PhD in Data-Driven Molecular Discovery Energy Storage

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DIFFER conducts leading fundamental research in the fields of fusion and chemical energy, in close partnership with academia and industry.

At the Dutch Institute for Fundamental Energy Research (DIFFER) we work on a future in which clean energy will be available to everybody, anywhere in the world. DIFFER’s mission is to perform leading fundamental research on materials, processes, and systems for a global sustainable energy infrastructure.

Our research focuses on two major energy themes: fusion energy as a clean, safe and sustainable energy source and chemical energy. We work in close partnership with (inter) national academia and industry. DIFFER is one of the ten research institutes of the Dutch Research Council (NWO).

Within our institute physicists, chemists, engineers, and other specialists work together in multidisciplinary teams to accelerate the transition to a sustainable society. DIFFER’s workforce is currently composed of ~160 scientists (of which 60 guests and interns), supported by ~40 technicians and ~4 0 support staff members.

The global nature of the energy challenge is apparent from the international representation of our employees, who originate from over 30 different countries. To strengthen our commitment to diversity, we formed a task force to design, implement, and monitor diversity and gender equality initiatives.

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.

Responsibilities:

• Develop and extend ML and physics-based workflows, such as Red Cat, 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…