Artificial Intelligent Atomic Force Microscope Enabled by Machine Learning

Abstract

Artificial intelligence (AI) and machine learning have promised to revolutionize the way we live and work, and one of particularly promising areas for AI is image analysis. Nevertheless, many current AI applications focus on post-processing of data, while in both materials sciences and medicines, it is often critical to respond to the data acquired on the fly. Here we demonstrate an artificial intelligent 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. We believe such a strategy empowered by machine learning is applicable to a wide range of instrumentations and broader physical machineries.

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• Advances of Machine Learning in Materials Science: Ideas and Techniques
• On machine learning analysis of atomic force microscopy images for image classification, sample surface recognition
• Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning
• Multi-modal machine learning analysis of GaSe molecular beam epitaxy growth conditions
• Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy