Jump to main content
Jump to site search

Issue 3, 2020
Previous Article Next Article

Automatic classification of single-molecule charge transport data with an unsupervised machine-learning algorithm

Author affiliations

Abstract

Single-molecule electrical characterization reveals the events occurring at the nanoscale, which provides guidelines for molecular materials and devices. However, data analysis to extract valuable information from the nanoscale measurement data remained as a major challenge. Herein, an unsupervised deep leaning method, a deep auto-encoder K-means (DAK) algorithm, is developed to distinguish different events from single-molecule charge transport measurements. As validated by three single-molecule junction systems, the method applies to the recognition for multiple compounds with various events and offers an effective data analysis method to track reaction kinetics at the single-molecule scale. This work opens the possibility of using deep unsupervised approaches to studying the physical and chemical processes at the single-molecule level.

Graphical abstract: Automatic classification of single-molecule charge transport data with an unsupervised machine-learning algorithm

Back to tab navigation

Supplementary files

Article information


Submitted
14 Aug 2019
Accepted
10 Dec 2019
First published
11 Dec 2019

Phys. Chem. Chem. Phys., 2020,22, 1674-1681
Article type
Paper

Automatic classification of single-molecule charge transport data with an unsupervised machine-learning algorithm

F. Huang, R. Li, G. Wang, J. Zheng, Y. Tang, J. Liu, Y. Yang, Y. Yao, J. Shi and W. Hong, Phys. Chem. Chem. Phys., 2020, 22, 1674
DOI: 10.1039/C9CP04496E

Social activity

Search articles by author

Spotlight

Advertisements