Unsupervised vector-based classification of single-molecule charge transport data
Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid
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Yn: Nature Communications, Cyfrol 7, 12922, 03.10.2016.
Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid
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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 -