Fuel cells

Accelerate your fuel cells research with resistivity and XRD measurements

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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:

  1. Ren, Yuan, Guang-Yi Cao, and Xin-Jian Zhu. "Predictive control of proton exchange membrane fuel cell (PEMFC) based on support vector regression machine." Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on. Vol. 7. IEEE, 2005.
  2.  El-Sharkh, M. Y., A. K. M. M. Rahman, and M. S. Alam. "Evolutionary programming-based methodology for economical output power from PEM fuel cell for micro-grid application." Journal of power sources 139.1-2 (2005): 165-169.

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CASE STUDY | Combinatorial Lab Accelerates Discovery with MZ

Bar-Ilan conductors hundreds of experiments to identify new photovoltaic compositions. Leveraging the MaterialsZone platform, researchers experienced an 85% cost reduction during innovation cycle.

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