AI aids design of microstructures in batteries and fuel cells

The machine learning algorithm from a team at Imperial College London reportedly allows researchers to explore possible designs for the microstructure of fuel cells and lithium-ion batteries, before running 3D simulations that help researchers make changes to improve performance.

Robust electrodes could pave the way to lighter electric vehicles

Improvements could include increasing the time between charges for electric vehicles and improving the power of hydrogen fuel cells running data centres.

A paper describing the work is published in npj Computational Materials.

The performance of fuel cells and lithium-ion batteries is closely related to their microstructure; the shape and arrangement pores inside their electrodes can affect how much power fuel cells can generate, and how quickly batteries charge and discharge. Because the micrometre-scale pores are so small, their specific shapes and sizes can be difficult to study at a high enough resolution to relate them to overall cell performance.

Now, researchers at Imperial have applied machine learning techniques to help them explore these pores virtually and run 3D simulations to predict cell performance based on their microstructure.