A Formulation-by-Design Framework for High-Performance Li-S Battery Electrolytes

This PhD project at University College London focuses on improving electrolyte formulations in lithium-sulfur (Li-S) batteries using advanced data-driven methodologies. Li-S batteries offer superior performance and sustainability compared to traditional Li-ion alternatives but face commercialization barriers due to complex chemistry and stability issues. The electrolyte critically influences electrode stability, charge/discharge rates, and material crossover but is currently optimized via slow empirical method. This project will develop a high-fidelity dataset capturing electrolyte properties such as phase behavior, ionic conductivity, and electrochemical stability. Predictive models will map formulation descriptors to experimentally validated battery performance mechanisms. Constrained multi-objective optimization (CMOO) will identify promising electrolyte formulations, subsequently validated by real cell testing. The student will be trained in both experimental techniques and computational modeling, gaining unique interdisciplinary skills in battery science, chemistry, and process systems engineering. Research will be conducted across several advanced research groups including the Advanced Propulsion Lab (APL), Electrochemical Innovation Lab (EIL), and Process Systems Engineering community. The project aims to accelerate discovery of robust, high-performance electrolytes enabling Li-S battery commercial viability. Outcomes include a validated dataset, predictive models linking formulations to battery mechanisms, and optimized electrolyte recipes demonstrated in lab-scale cells. Developing efficient and sustainable energy storage technologies is critical for a low-carbon future. Li-S batteries promise higher energy density and environmental benefits over current batteries but require breakthroughs in electrolyte design. This project combines cutting-edge data science with electrochemistry to overcome these barriers, catalyzing industry adoption and advancing clean energy technologies.

Candidates should hold or expect to obtain a degree (1st, 2:1, or M.Sc.) in chemical engineering, chemistry, materials science, physics or related disciplines. UK citizenship or eligibility for home fees is required. Experience in electrochemistry, battery science, numerical optimisation, or process systems engineering is desirable. Effective communication and ability to work independently and collaboratively is essential.

Apply via the UCL online application portal at https://evision.ucl.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RRDCENSING01&code2=0041. Nominate Dr Alexander Kibler as supervisor and include a statement of interest. For informal enquiries contact: a.kibler@ucl.ac.uk. The closing date is 1 May 2026, but the position will be filled as soon as a suitable candidate is found.