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A reference-free clustering method for the analysis of molecular break-junction measurements. / Cabosart, Damien; El Abbassi, Maria; Stefani, Davide; Frisenda, Riccardo; Calame, Michel; Van der Zant, Herre S.J.; Perrin, Mickael L.

In: Applied Physics Letters, Vol. 114, No. 14, 143102, 2019.

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@article{a93f359600494f2696fa0bfe4fd6f98e,
title = "A reference-free clustering method for the analysis of molecular break-junction measurements",
abstract = "Single-molecule break-junction measurements are intrinsically stochastic in nature, requiring the acquisition of large datasets of “breaking traces” to gain insight into the generic electronic properties of the molecule under study. For example, the most probable conductance value of the molecule is often extracted from the conductance histogram built from these traces. In this letter, we present an unsupervised and reference-free machine learning tool to improve the determination of the conductance of oligo(phenylene ethynylene)dithiol from mechanically controlled break-junction (MCBJ) measurements. Our method allows for the classification of individual breaking traces based on an image recognition technique. Moreover, applying this technique to multiple merged datasets makes it possible to identify common breaking behaviors present across different samples, and therefore to recognize global trends. In particular, we find that the variation in the extracted molecular conductance can be significantly reduced resulting in a more reliable estimation of molecular conductance values from MCBJ datasets. Finally, our approach can be more widely applied to different measurement types which can be converted to two-dimensional images.",
author = "Damien Cabosart and {El Abbassi}, Maria and Davide Stefani and Riccardo Frisenda and Michel Calame and {Van der Zant}, {Herre S.J.} and Perrin, {Mickael L.}",
year = "2019",
doi = "10.1063/1.5089198",
language = "English",
volume = "114",
journal = "Applied Physics Letters",
issn = "0003-6951",
publisher = "American Institute of Physics Publising LLC",
number = "14",

}

RIS

TY - JOUR

T1 - A reference-free clustering method for the analysis of molecular break-junction measurements

AU - Cabosart, Damien

AU - El Abbassi, Maria

AU - Stefani, Davide

AU - Frisenda, Riccardo

AU - Calame, Michel

AU - Van der Zant, Herre S.J.

AU - Perrin, Mickael L.

PY - 2019

Y1 - 2019

N2 - Single-molecule break-junction measurements are intrinsically stochastic in nature, requiring the acquisition of large datasets of “breaking traces” to gain insight into the generic electronic properties of the molecule under study. For example, the most probable conductance value of the molecule is often extracted from the conductance histogram built from these traces. In this letter, we present an unsupervised and reference-free machine learning tool to improve the determination of the conductance of oligo(phenylene ethynylene)dithiol from mechanically controlled break-junction (MCBJ) measurements. Our method allows for the classification of individual breaking traces based on an image recognition technique. Moreover, applying this technique to multiple merged datasets makes it possible to identify common breaking behaviors present across different samples, and therefore to recognize global trends. In particular, we find that the variation in the extracted molecular conductance can be significantly reduced resulting in a more reliable estimation of molecular conductance values from MCBJ datasets. Finally, our approach can be more widely applied to different measurement types which can be converted to two-dimensional images.

AB - Single-molecule break-junction measurements are intrinsically stochastic in nature, requiring the acquisition of large datasets of “breaking traces” to gain insight into the generic electronic properties of the molecule under study. For example, the most probable conductance value of the molecule is often extracted from the conductance histogram built from these traces. In this letter, we present an unsupervised and reference-free machine learning tool to improve the determination of the conductance of oligo(phenylene ethynylene)dithiol from mechanically controlled break-junction (MCBJ) measurements. Our method allows for the classification of individual breaking traces based on an image recognition technique. Moreover, applying this technique to multiple merged datasets makes it possible to identify common breaking behaviors present across different samples, and therefore to recognize global trends. In particular, we find that the variation in the extracted molecular conductance can be significantly reduced resulting in a more reliable estimation of molecular conductance values from MCBJ datasets. Finally, our approach can be more widely applied to different measurement types which can be converted to two-dimensional images.

UR - http://www.scopus.com/inward/record.url?scp=85064115226&partnerID=8YFLogxK

U2 - 10.1063/1.5089198

DO - 10.1063/1.5089198

M3 - Article

VL - 114

JO - Applied Physics Letters

T2 - Applied Physics Letters

JF - Applied Physics Letters

SN - 0003-6951

IS - 14

M1 - 143102

ER -

ID: 53252427