In this section
@article{Lemmer:2016:10.1038/ncomms12922,
author = {Lemmer, M and Inkpen, MS and Kornysheva, K and Long, NJ and Albrecht, T},
doi = {10.1038/ncomms12922},
journal = {Nature Communications},
pages = {1--10},
title = {Unsupervised vector-based classification of single-molecule charge transport data},
url = {http://dx.doi.org/10.1038/ncomms12922},
volume = {7},
year = {2016}
}
TY - JOUR
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.
AU - Lemmer,M
AU - Inkpen,MS
AU - Kornysheva,K
AU - Long,NJ
AU - Albrecht,T
DO - 10.1038/ncomms12922
EP - 10
PY - 2016///
SN - 2041-1723
SP - 1
TI - Unsupervised vector-based classification of single-molecule charge transport data
T2 - Nature Communications
UR - http://dx.doi.org/10.1038/ncomms12922
UR - https://www.nature.com/articles/ncomms12922
UR - http://hdl.handle.net/10044/1/41512
VL - 7
ER -
Professor Nick Long
Email: n.long@imperial.ac.uk
Telephone: +44 (0)20 7594 5781
501J
Molecular Sciences Research Hub
White City Campus
