Selected article for: "abstract set and machine learning"
Author: Azuri, Ido; Rosenhek-Goldian, Irit; Regev-Rudzki, Neta; Fantner, Georg; Cohen, Sidney R
Title: The role of convolutional neural networks in scanning probe microscopy: a review
  • Cord-id: znonkrd2
  • Document date: 2021_8_13
    • ID: znonkrd2
    Snippet: Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy me
    Document: Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.

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