Open AccessMachine learning at the (sub)atomic scale: next generation scanning probe microscopy
Author(s) -
Oliver Gordon,
Philip Moriarty
Publication year - 2020
Publication title -
machine learning: science and technology
Language(s) - English
Resource type - Journals
ISSN - 2632-2153
DOI - 10.1088/2632-2153/ab7d2f
Subject(s) - microscopy , nanotechnology , scanning probe microscopy , scanning tunneling microscope , atomic units , atomic force microscopy , scanning capacitance microscopy , focus (optics) , scanning ion conductance microscopy , materials science , scale (ratio) , nanoscopic scale , computer science , artificial intelligence , physics , scanning confocal electron microscopy , optics , quantum mechanics
We discuss the exciting prospects for a step change in our ability to map and modify matter at the atomic/molecular level by embedding machine learning algorithms in scanning probe microscopy (with a particular focus on scanning tunnelling microscopy, STM). This nano-AI hybrid approach has the far-reaching potential to realise a technology capable of the automated analysis, actuation, and assembly of matter with a precision down to the single chemical bond limit.