Issue 45, 2023

Machine learning and analytical methods for single-molecule conductance measurements

Abstract

Single-molecule measurements of single-molecule conductance between metal nanogap electrodes have been actively investigated for molecular electronics, biomolecular analysis, and the search for novel physical properties at the nanoscale level. While it is a disadvantage that single-molecule conductance measurements exhibit easily fluctuating and unreliable conductance, they offer the advantage of rapid, repeated acquisition of experimental data through the repeated breaking and forming of junctions. Owing to these characteristics, recently developed informatics and machine learning approaches have been applied to single-molecule measurements. Machine learning-based analysis has enabled detailed analysis of individual traces in single-molecule measurements and improved its performance as a method of molecular detection and identification at the single-molecule level. The novel analytical methods have improved the ability to investigate for new chemical and physical properties. In this review, we focus on the analytical methods for single-molecule measurements and provide insights into the methods used for single-molecule data interrogation. We present experimental and traditional analytical methods for single-molecule measurements, provide examples of each type of machine learning method, and introduce the applicability of machine learning to single-molecule measurements.

Graphical abstract: Machine learning and analytical methods for single-molecule conductance measurements

Article information

Article type
Feature Article
Submitted
30 mar 2023
Accepted
02 mai 2023
First published
02 mai 2023
This article is Open Access
Creative Commons BY license

Chem. Commun., 2023,59, 6796-6810

Machine learning and analytical methods for single-molecule conductance measurements

Y. Komoto, J. Ryu and M. Taniguchi, Chem. Commun., 2023, 59, 6796 DOI: 10.1039/D3CC01570J

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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