Sam Cooper is a Senior Lecturer in the Dyson School of Design Engineering, Imperial College London.

Sam Cooper is a Senior Lecturer in the Dyson School of Design Engineering, where he lead the TLDR group (Tools for Learning, Design, and Research). The TLDR group largely focus on the development of methods for characterising, simulating, and optimising energy systems. Over the past five years, the group have released a variety open-source software that enable the research community to rapidly analyse experimental data to extract materials’ properties. Their recently papers demonstrate how machine learning methods can be used for dimensionality expansion (2D to 3D image generation) and data fusion (combining high-res 2D data with low-res 3D data). In this talk, Sam Cooper will explain a modular workflow for designing optimised battery electrodes.

Visit the group’s webpage to find out more: If you would like to join the group and collaborate – do get in touch via email. 

Talk Title

Machine learning for the characterisation and design of battery electrodes

Talk Summary

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; “VoxCel” an open-source, voxel-based, GPU-accelerated, multi-physics cell simulation; 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. Lastly, I’ll present a webapp that normalises the data obtained from testing cells in a lab for easy comparison to commercial cells: cell-normaliser.