Artificial Intelligent Atomic Force Microscope Enabled by Machine Learning

TL;DR

This paper presents an AI-enabled atomic force microscope that uses machine learning for real-time pattern recognition and adaptive experimentation, significantly advancing autonomous data analysis in materials science.

Contribution

Introduction of an AI-AFM capable of real-time data classification and adaptive probing, enabling autonomous experimentation without human intervention.

Findings

Real-time pattern recognition in ferroelectric materials

Adaptive experimentation at domain walls and grain boundaries

Potential applicability to various physical instruments

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.