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Unsupervised vector-based classification of single-molecule charge transport data. / Lemmer, Mario; Inkpen, Michael S; Kornysheva, Katja; Long, Nicholas J; Albrecht, Tim.

In: Nature Communications, Vol. 7, 12922, 03.10.2016.

Research output: Contribution to journalArticle

HarvardHarvard

Lemmer, M, Inkpen, MS, Kornysheva, K, Long, NJ & Albrecht, T 2016, 'Unsupervised vector-based classification of single-molecule charge transport data', Nature Communications, vol. 7, 12922. https://doi.org/10.1038/ncomms12922

APA

Lemmer, M., Inkpen, M. S., Kornysheva, K., Long, N. J., & Albrecht, T. (2016). Unsupervised vector-based classification of single-molecule charge transport data. Nature Communications, 7, [12922]. https://doi.org/10.1038/ncomms12922

CBE

Lemmer M, Inkpen MS, Kornysheva K, Long NJ, Albrecht T. 2016. Unsupervised vector-based classification of single-molecule charge transport data. Nature Communications. 7:Article 12922. https://doi.org/10.1038/ncomms12922

MLA

VancouverVancouver

Lemmer M, Inkpen MS, Kornysheva K, Long NJ, Albrecht T. Unsupervised vector-based classification of single-molecule charge transport data. Nature Communications. 2016 Oct 3;7. 12922. https://doi.org/10.1038/ncomms12922

Author

Lemmer, Mario ; Inkpen, Michael S ; Kornysheva, Katja ; Long, Nicholas J ; Albrecht, Tim. / Unsupervised vector-based classification of single-molecule charge transport data. In: Nature Communications. 2016 ; Vol. 7.

RIS

TY - JOUR

T1 - Unsupervised vector-based classification of single-molecule charge transport data

AU - Lemmer, Mario

AU - Inkpen, Michael S

AU - Kornysheva, Katja

AU - Long, Nicholas J

AU - Albrecht, Tim

PY - 2016/10/3

Y1 - 2016/10/3

N2 - 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.

AB - 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.

KW - Journal Article

U2 - 10.1038/ncomms12922

DO - 10.1038/ncomms12922

M3 - Article

C2 - 27694904

VL - 7

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

M1 - 12922

ER -