An artificial intelligence 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.

Towards a Fully Automated Scanning Probe Microscope for Biomedical Applications

“…The second level of automation includes sample and cantilever exchange. This level is essential to enable 24/7 continuous operations, and future integration with artificial intelligence (AI) [ 60 ]. Incidentally, this level is the most challenging in terms of instrument design.…”

Artificial-intelligence-driven scanning probe microscopy

“…Future work can further extend our method by combining it with semi-automatic ML approaches used for, e.g., the identification of adverse imaging conditions 24,33 or imaging regions of interest 18 .…”

Machine learning at the (sub)atomic scale: next generation scanning probe microscopy

“…Atomic force microscopy also has its own variants of spectroscopy and spectromicroscopy 4 including single point force-distance curves (where 'distance' refers to the tip-sample separation), force-distance maps, potential energy landscapes, damping/dissipation variations, phase maps, Kelvin probe force microscopy, and higher harmonic signal variations as a function of both lateral and vertical position of the probe. Taken together, these various information channels provide an exceptionally rich multimodal (or hyperspectral) multidimensional dataset, which, either in isolation or combined with image data, can be mined via machine learning strategies to not only provide significant improvements in both post-experiment [37] and real-time [45] effective signal-to-noise ratio but, importantly, to classify and determine material properties at the nanoscale and below. Burzawa et al [46] have taken his strategy one step further and adopted machine learning not to extract materials properties but to determine which particular physical model/dynamics drives pattern formation in a system (in their case, the 2D Ising model).…”