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An artificial intelligence atomic force microscope enabled by machine learning

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Abstract

Artificial intelligence (AI) and machine learning have promised to revolutionize the way we live and work, and one of the particularly promising areas for AI is image analysis. Nevertheless, many current AI applications focus on the post-processing of data, while in both materials sciences and medicine, it is often critical to respond to the data acquired on the fly. Here we demonstrate an artificial intelligence atomic force microscope (AI-AFM) that is capable of not only pattern recognition and feature identification in ferroelectric materials and electrochemical systems, but can also respond to classification via adaptive experimentation with additional probing at critical domain walls and grain boundaries, all in real time on the fly without human interference. Key to our success is a highly efficient machine learning strategy based on a support vector machine (SVM) algorithm capable of high fidelity pixel-by-pixel recognition instead of relying on the data from full mapping, making real time classification and control possible during scanning, with which complex electromechanical couplings at the nanoscale in different material systems can be resolved by AI. For AFM experiments that are often tedious, elusive, and heavily rely on human insight for execution and analysis, this is a major disruption in methodology, and we believe that such a strategy empowered by machine learning is applicable to a wide range of instrumentations and broader physical machineries.

Graphical abstract: An artificial intelligence atomic force microscope enabled by machine learning

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Publication details

The article was received on 20 Aug 2018, accepted on 20 Oct 2018 and first published on 22 Oct 2018


Article type: Paper
DOI: 10.1039/C8NR06734A
Citation: Nanoscale, 2018, Advance Article
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    An artificial intelligence atomic force microscope enabled by machine learning

    B. Huang, Z. Li and J. Li, Nanoscale, 2018, Advance Article , DOI: 10.1039/C8NR06734A

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