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Deep Learning To Classify And Establish Structure Property Predictions With PeakForce QNM Atomic Force Microscopy

An example of deep learning used with atomic force microscope.

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Machine learning and specifically, deep learning, is a powerful tool to establish the presence (or absence) of microstructure correlations to bulk properties with its ability to flesh out relationships and trends that are difficult to establish otherwise.
This application note discusses the use of deep learning tools, to explore AFM phase and PeakForce Quantitative Nanomechanics (QNM) images of impact copolymers, a polymer blend of polypropylene with micro-sized domains of rubber.
Click here to read more. Associated webinar is here.


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