Uncovering hierarchical data structure in single molecule transport

Ben H. Wu, Jeffrey A. Ivie, Tyler K. Johnson, Oliver L.A. Monti

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Interpretation of single molecule transport data is complicated by the fact that all such data are inherently highly stochastic in nature. Features are often broad, seemingly unstructured and distributed over more than an order of magnitude. However, the distribution contains information necessary for capturing the full variety of processes relevant in nanoscale transport, and a better understanding of its hierarchical structure is needed to gain deeper insight into the physics and chemistry of single molecule electronics. Here, we describe a novel data analysis approach based on hierarchical clustering to aid in the interpretation of single molecule conductance-displacement histograms. The primary purpose of statistically partitioning transport data is to provide avenues for unbiased hypothesis generation in single molecule break junction experiments by revealing otherwise potentially hidden aspects in the conductance data. Our approach is generalizable to the analysis of a wide variety of other single molecule experiments in molecular electronics, as well as in single molecule fluorescence spectroscopy, force microscopy, and ion-channel conductance measurements.

Original languageEnglish (US)
Article number092321
JournalJournal of Chemical Physics
Issue number9
StatePublished - Mar 7 2017

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry


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