Machine learning for the characterisation and design of electrode materials
Samuel Cooper a, Steve Kench b, Isaac Squires b, Amir Dahari a, Ronan Docherty a, Sam Hair a
a Dyson School of Design Engineering, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
b Polaron, London, UK (www.polaron.ai)
Proceedings of 24th International Conference on Solid State Ionics (SSI24)
Advanced characterisation techniques: fundamental and devices
London, United Kingdom, 2024 July 14th - 19th
Organizers: John Kilner and Stephen Skinner
Invited Speaker, Samuel Cooper, presentation 479
Publication date: 10th April 2024

Battery companies want to know the relationship between their manufacturing parameters and the performance of the resulting cells, so that they can optimise their products for particular applications, reduce costs, and improve yield. The literature contains many examples of physics-based models of the various manufacturing processes (including mixing, coating, drying and calendaring), but these systems are hugely complex, and as a result they are expensive to simulate and hard to validate.

Recent advances in generative machine learning (ML) methods have allowed the relationship from manufacturing parameters to microstructure to be directly learned from data.

In this talk I will present a modular approach to electrode microstructural optimisation cycle that makes use of these ML methods, in combination with GPU accelerated metric extraction (TauFactor 2), electrochemical cell simulation (PyBaMM), and Bayesian optimisation. We are always looking for new collaborations and new data so please get in touch! If you’d like to use any of our suite of open-source tools, then head to our website: https://tldr-group.github.io

We’ve also just spun-out a company from Imperial, called Polaron AI, to bring these tools to market. Check out our website (www.polaron.ai) and get in touch: info@polaron.ai

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