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Faraday Institution Studentship - Microstructural fingerprint: The application of machine learning methods for the characterisation and optimisation of electrode microstructures

  • Full or part time
  • Application Deadline
    Sunday, June 30, 2019
  • Funded PhD Project (UK Students Only)
    Funded PhD Project (UK Students Only)

Project Description

The performance of lithium ion batteries is linked to the 3D microstructure of their porous electrodes. Advances in the field of micro/nano-tomography have enabled researchers to capture the morphologies of these microstructure at a resolution relevant to needs of multiphysics simulation [1]; however, the robust characterisation and analysis of this data remains a challenge.

Recent advances in machine learning have seen the development of novel image generation tools. In particular, these include style transfer using hierarchical neural architectures [2], variational autoencoders [3] and adversarial methods [4]. These concepts have been developing rapidly in the context of 2D colour images over the past 5 years but have rarely been applied to the generation of 3D labelled microstructural data.

This project would seek to transfer the power of these methods to the field of microstructural analysis and generation. First by enabling the extraction of a compressed representations (a “fingerprint”) of these memory intensive 3D tomography volumes and then using these representations to more efficiently explore the space of possible microstructure to find new optimal configurations.

This will link up with the significant tomographic investigations underway in both the multiscale modelling and degradation fast-start projects, as well as interacting with the continuum modelling efforts seeking to build simplified models explaining cell performance.

Imperial College is committed to equality of opportunity, to eliminating discrimination and to creating an inclusive working environment. We are an Athena SWAN Silver award winner, a Stonewall Diversity Champion, a Disability Confident Employer and work in partnership with GIRES to promote respect for trans people.

Funding Notes

The Faraday Institution Cluster PhD students receive an enhanced stipend over and above the standard EPSRC offer. The total annual stipend is approximately £20,000 (plus London weighting) plus an additional £7,000 annually to cover training and travel costs. Recipients will have access to multiple networking opportunities, industry visits, mentorship, internships, as well as quality experiences that will further develop knowledge, skills, and aspirations.

References

1. Daemi SR, Tan C, Volkenandt T, Cooper SJ, Palacios-Padros A, Cookson J, et al. Visualizing the Carbon Binder Phase of Battery Electrodes in Three Dimensions. ACS Appl Energy Mater. American Chemical Society; 2018 Aug 27;1(8):3702–10.
2. Snelgrove X. High-resolution multi-scale neural texture synthesis. In: SIGGRAPH Asia 2017 Technical Briefs on - SA ’17. New York, New York, USA: ACM Press; 2017. p. 1–4.
3. Cang R, Li H, Yao H, Jiao Y, Ren Y. Improving direct physical properties prediction of heterogeneous materials from imaging data via convolutional neural network and a morphology-aware generative model. Comput Mater Sci. 2018 Jul;150:212–21.
4. Weber T, Racanière S, Reichert DP, Buesing L, Guez A, Rezende DJ, et al. Imagination-Augmented Agents for Deep Reinforcement Learning. 2017 Jul 19;

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