Fuel cells are electrochemical cells wich convert fuels (generally hydrogen and oxygen) into electricity through electrochemical reactions. A fuel cell is composed of a cathode, an anode, and electrolyte, where each of these can be synthesised from different materials. Finding the ideal materials for each of the components of the fuel cell is critical for the energy efficiency of the fuel cell. This database including millions of optical, sheet resistance, composition, XRD, and Raman measurements for thousands of materials which can accelerate fuel cell research and development.
Accelerating fuel cell R&D with machine learning
Machine learning algorithms in combination with novel experimental data can accelerate the process of materials design and discovery for fuel cells applications. Using machine learning algorithms on experimental data we can discover new materials which can serve as better anodes and/or cathodes, get ‘materials recipes’ for synthesizing materials, and build tools to automatically analyze new measurements to retrieve insights. This could help design new fuel cells with higher energy efficiencies which will greatly affect the fuel cell industry.
See what’s been done with machine learning for fuel cells: