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I am a PhD student in the Johansson research group at the Department of Physics, Chalmers University of Technology, supervised by Professor Patrik Johansson. Our group’s research focuses on understanding novel battery materials through extensive experimental characterization and advanced simulation techniques. My project, co-supervised by Professor Ivano Castelli from the Technical University of Denmark, uses data-driven methods to model new types of electrolytes. Before starting my PhD, I completed a Bachelor’s degree in Engineering Physics and a Master’s in Physics at Chalmers.

Our interest in developing novel electrolytes is motivated by the need for alternative battery chemistries to complement today’s lithium-ion batteries. While lithium-ion batteries currently dominate the market, they have limitations in terms of resource availability, theoretical capacity, and safety. There are several promising electrode materials that could address these limitations, but they often require new electrolyte concepts to function effectively. The electrolyte is a critical component in any battery, as it enables ion transport between the electrodes. However, identifying electrolytes that are both highly conductive and chemically stable with both electrodes remains a significant challenge.

A purely experimental approach to discovering new electrolytes would be infeasible, while classical computational methods struggle to explore the vast range of possible configurations efficiently. Therefore, my project employs data-driven approaches such as machine learning and symbolic regression, allowing us to explore and understand the chemical space more effectively. A particular aim is to connect molecular descriptors to macroscopic properties and try to gain analytical insights through a data-driven approach.