UOG
The Self-Driving Microscope: Predicting Stochastic Failure in Solid-State Batteries using Physics-Informed AI (Ref: M34Impact-MSE2)
✓ Funded (Competition)
⏰ Closing Soon
🎓 Computational Mathematics
🎓 Computational Physics
🎓 Energy Technologies
🎓 Materials Science
🎓 Mathematical Modelling
🎓 Solid State Physics
battery failure
beamlines
generative ai
graph neural networks
materials modelling
physics informed ai
solid state batteries
x-ray tomography
Funded PhD at the University of Greenwich developing physics-informed AI to predict failure in solid-state batteries using X-ray imaging, simulations, and generative models.
Project Description
This PhD project focuses on predicting stochastic failure in solid-state batteries using physics-informed AI. It forms part of a major research initiative aimed at developing a self-driving microscope capable of identifying hidden failure mechanisms in real time.
The research integrates AI, physics-based simulation, and materials science to detect microscopic flaws such as dendrite formation, cracking, and short-circuiting in battery materials.
Key components include:
Building multi-scale datasets from 3D X-ray tomography of battery cells
Developing AI models (e.g., Graph Neural Networks) to identify failure precursors
Integrating physics-based simulations to derive features such as tortuosity and ionic flux
Using generative AI (diffusion models/GANs) to augment datasets
Working with experimental data from national facilities such as Diamond Light Source
The project is part of the M34Impact programme and contributes to the development of autonomous scientific instruments. The student will be embedded in the BASE Laboratory with links to Rutherford Appleton Laboratory.
Entry Requirements
Not explicitly specified
How to Apply
Apply via University of Greenwich PhD application process
Include required academic documents
Include required academic documents
Eligibility
UK/Home
EU
International
Supervisor Profile
DJ
Dr James Le Houx, Dr Andrew Kao, Dr Mikhail Poluektov
University of Greenwich, Faculty of Engineering and Science
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