Accelerate your battery research with resistivity, cyclic voltammetry, and XRD measurements

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Batteries are devices consisting of one or more electrochemical cells connected to appliances such as flashlights, smartphones,  and electric cars, to provide power to these appliances. A battery is composed of an anode, a cathode and electrolyte solution. Finding ideal materials for the cathode and anode is important for increasing the batteries capacity and lifetime. Metal oxides are promising materials for battery anodes and cathodes and many efforts are being done into studying different oxides for this application. This database includes measurements of thousands of oxides created from over a hundred starting materials. The  XRD and Resistivity measurements can accelerate the discovery of new cathode and anode materials and development of batteries with better capacity and lifetime.

Accelerating battery R&D with machine learning

Machine learning algorithms in combination with novel experimental data can accelerate the process of materials design and discovery for battery 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 batteries with better capacity and lifetime which will greatly affect the battery industry.

See what’s been done with machine learning for batteries:

  1. Sendek, Austin D., et al. "Holistic computational structure screening of more than 12000 candidates for solid lithium-ion conductor materials." Energy & Environmental Science 10.1 (2017): 306-320.
  2. Shandiz, M. Attarian, and R. Gauvin. "Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries." Computational Materials Science 117 (2016): 270-278.
  3. Hu, Xiaosong, Shengbo Eben Li, and Yalian Yang. "Advanced machine learning approach for lithium-ion battery state estimation in electric vehicles." IEEE Transactions on Transportation electrification 2.2 (2016): 140-149.

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CASE STUDY | Combinatorial Lab Accelerates Discovery with MZ

Bar-Ilan conducts 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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