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:
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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