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

Mario Lemmer, Michael S. Inkpen, Katja Kornysheva, Nicholas J. Long and Tim Albrecht ()
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Mario Lemmer: Imperial College London
Michael S. Inkpen: Imperial College London
Katja Kornysheva: Institute for Cognitive Neuroscience, University College London
Nicholas J. Long: Imperial College London
Tim Albrecht: Imperial College London

Nature Communications, 2016, vol. 7, issue 1, 1-10

Abstract: 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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms12922

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DOI: 10.1038/ncomms12922

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