Unsupervised vector-based classification of single-molecule charge transport data

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Title: Unsupervised vector-based classification of single-molecule charge transport data
Authors: Lemmer, M
Inkpen, MS
Kornysheva, K
Long, NJ
Albrecht, T
Item Type: Journal Article
Abstract: The stochastic nature of single-molecule charge transport measurements requires collection of large data sets to capture the full complexity of a molecular system. Data analysis is then guided by certain expectations, for example, a plateau feature in the tunnelling current distance trace, and the molecular conductance extracted from suitable histogram analysis. However, differences in molecular conformation or electrode contact geometry, the number of molecules in the junction or dynamic effects may lead to very different molecular signatures. Since their manifestation is a priori unknown, an unsupervised classification algorithm, making no prior assumptions regarding the data is clearly desirable. Here we present such an approach based on multivariate pattern analysis and apply it to simulated and experimental single-molecule charge transport data. We demonstrate how different event shapes are clearly separated using this algorithm and how statistics about different event classes can be extracted, when conventional methods of analysis fail.
Issue Date: 3-Oct-2016
Date of Acceptance: 16-Aug-2016
ISSN: 2041-1723
Publisher: Nature Publishing Group
Journal / Book Title: Nature Communications
Volume: 7
Copyright Statement: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit
Sponsor/Funder: The Leverhulme Trust
Funder's Grant Number: RPG-2012-754
Keywords: MD Multidisciplinary
Publication Status: Published
Open Access location:
Article Number: 12922
Appears in Collections:Chemistry
Catalysis and Advanced Materials

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