Print Email Facebook Twitter A reference-free clustering method for the analysis of molecular break-junction measurements Title A reference-free clustering method for the analysis of molecular break-junction measurements Author Cabosart, D.C.N. (TU Delft QN/van der Zant Lab; TU Delft QN/Quantum Nanoscience; Kavli institute of nanoscience Delft) El Abbassi, M. (TU Delft QN/van der Zant Lab; TU Delft QN/Quantum Nanoscience; Kavli institute of nanoscience Delft) Stefani, D. (TU Delft QN/van der Zant Lab; TU Delft QN/Quantum Nanoscience; Kavli institute of nanoscience Delft) Frisenda, Riccardo (Instituto de Ciencia de Materiales de Madrid (ICMM)) Calame, Michel (Swiss Federal Laboratories for Materials Science and Technology (Empa); University of Basel) van der Zant, H.S.J. (TU Delft QN/van der Zant Lab; TU Delft QN/Quantum Nanoscience; Kavli institute of nanoscience Delft) Perrin, M.L. (Swiss Federal Laboratories for Materials Science and Technology (Empa)) Department QN/Quantum Nanoscience Date 2019 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. To reference this document use: http://resolver.tudelft.nl/uuid:a93f3596-0049-4f26-96fa-0bfe4fd6f98e DOI https://doi.org/10.1063/1.5089198 ISSN 0003-6951 Source Applied Physics Letters, 114 (14) Part of collection Institutional Repository Document type journal article Rights © 2019 D.C.N. Cabosart, M. El Abbassi, D. Stefani, Riccardo Frisenda, Michel Calame, H.S.J. van der Zant, M.L. Perrin Files PDF 1.5089198.pdf 1.47 MB Close viewer /islandora/object/uuid:a93f3596-0049-4f26-96fa-0bfe4fd6f98e/datastream/OBJ/view