Nano materials

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Nanomaterials are materials possessing grain size less than 100 nanometers (100 nm).  The fascinating and unique, chemical, physical, and mechanical properties, make them an ideal candidate in a wide variety of applications such as  Flat-Panel displays, better cutting tools, batteries,   magnets, sensors, machinable ceramics. This database consisting of millions of optical, composition, XRD, and Raman measurements of nanolayers can accelerate research into nanomaterials and lead to new breakthroughs.

Accelerating nanomaterials R&D with machine learning

Machine learning algorithms in combination with novel experimental data can accelerate the process of materials design and discovery for nanomaterials. Using machine learning algorithms on experimental data we can discover new nanomaterials, get ‘materials recipes’ for synthesizing nanomaterials with interesting properties, and build tools to automatically analyze new measurements to retrieve insights. This could help design new nanomaterials for a wide range of applications including batteries,  magnets, and sensors.

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

  1. Winkler, D. A., et al. "Modelling and predicting the biological effects of nanomaterials." SAR and QSAR in Environmental Research 25.2 (2014): 161-172.

Goldberg, Eli, et al. "Prediction of nanoparticle transport behavior from physicochemical properties: machine learning provides insights to guide the next generation of transport models." Environmental Science: Nano 2.4 (2015): 352-360.

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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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