AI-enabled Digital Design of Sodium-ion Batteries Using Advanced Multi-scale Computing and 3D Characterisation Techniques
Not stated
- Funding
- Self-Funded PhD Students Only
- Application deadline
- Year-round applications
About the project
About the Project Sodium-ion batteries are emerging as a scalable and resource-resilient technology for stationary energy storage and low-cost electrified transport, yet their commercial deployment is still limited by coupled materials, interface, microstructure and cell-level challenges. This PhD project will develop an AI-enabled multiscale computational framework, integrated with advanced three-dimensional characterisation, to guide the design and structural optimisation of high-performance sodium-ion battery electrodes and cells. The project will connect atomistic mechanisms to electrode-scale behaviour across four mutually reinforcing levels. A distinctive feature of the project is the integration of AI computation with multiscale characterisation such as 3D-FIB-SEM and Nano X-ray CT. The student will benefit from the research environment at the University of Birmingham, including access to the Facility for Electron Microscopy and national HPC computational resources. The project is suitable for candidates with backgrounds in materials science, chemistry, chemical engineering, mechanical engineering, computational modelling, physics or related disciplines, and will provide training in atomistic simulation, machine learning, image-based modelling, high-performance computing and advanced battery characterisation.