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

Oliver Gordon | Zendy; Philip Moriarty | Zendy

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Machine 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) - scanning tunneling microscope , microscopy , scanning probe microscopy , scanning capacitance microscopy , nanotechnology , scanning ion conductance microscopy , atomic units , focus (optics) , atomic force microscopy , conductive atomic force microscopy , materials science , embedding , computer science , scale (ratio) , 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.

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