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 the cell 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. In addition, I will be introducing a new kintsugi SEM imaging method for accurately observing the nanostructure of the carbon binder domain; ML methods for generating 3D data from 2D images, as well as, inpainting artefacts in image data; and a data fusion method for combining multi-modal datasets using GANs. 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 References [1] Kench, S., Squires, I., Dahari, A., Planella, F. B., Roberts, S. A., & Cooper, S. J. (2024). Li-ion battery design through microstructural optimization using generative AI. Matter. [2] Kench, S., & Cooper, S. J. (2021). Generating three-dimensional structures from a two-dimensional slice with generative adversarial network-based dimensionality expansion. Nature Machine Intelligence, 3(4), 299-305. [3] Dahari, A., Kench, S., Squires, I., & Cooper, S. J. (2023). Fusion of complementary 2D and 3D mesostructural datasets using generative adversarial networks. Advanced Energy Materials, 13(2), 2202407.