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

Damien Cabosart,Mickael L Perrin,Herre S J Van Der Zant,Michel Calame,Maria El Abbassi,Davide Stefani,Riccardo Frisenda

doi:10.1063/1.5089198

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.

Highlights

Each breaking trace provides information about the conformation and configuration of the junction.6 it is only through the statistical analysis of thousands of such traces, that an in-depth mapping of the breaking dynamics7–9 and a meaningful interpretation of the molecular junction behavior can be obtained
The most probable conductance value of the molecule is often extracted from the conductance histogram built from these traces
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

Summary

Introduction

Each breaking trace provides information about the conformation and configuration of the junction.6 it is only through the statistical analysis of thousands of such traces, that an in-depth mapping of the breaking dynamics7–9 and a meaningful interpretation of the molecular junction behavior can be obtained.10–12. 11 different datasets have been recorded with a bias voltage of 100 mV, each exhibiting different molecular yields, varying from 3% to 63%.14 The inset of Fig. 1 presents the extracted value of GM for all of them.

Results

Conclusion