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

doi:10.1088/2632-2153/ab7d2f

2020-05-18

Oliver Gordon *

Philip Moriarty

DOI：10.1088/2632-2153/ab7d2f

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摘要

En 中文

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.

Keyword:

CNNs

automated SPM

SPM state recognition

期刊

Machine Learning-Science and Technology

IF:

4.6

论文数:

1.1K

被引数:

3.4K

机构

University of Nottingham

学者数:

3.4W

论文数: 3.2W

被引数: 5.5W

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