Print Email Facebook Twitter A generalized neural network approach for separation of molecular breaking traces Title A generalized neural network approach for separation of molecular breaking traces Author van Veen, F. (TU Delft QN/van der Zant Lab; Swiss Federal Laboratories for Materials Science and Technology (Empa)) Ornago, L. (TU Delft QN/van der Zant Lab) van der Zant, H.S.J. (TU Delft QN/van der Zant Lab) El Abbassi, M. (TU Delft QN/van der Zant Lab) Date 2023 Abstract Break-junction experiments are used to statistically study the electronic properties of individual molecules. The measurements consist of repeatedly breaking and merging a gold wire while measuring the conductance as a function of displacement. When a molecule is captured, a plateau is observed in the conductance traces otherwise exponentially decaying tunnel traces are measured. Clustering methods are widely used to separate these traces and identify potential sub-populations in the data corresponding to different molecular junction configurations. As these configurations are typically a priori unknown, unsupervised methods are most suitable for the classification. However, most of the unsupervised methods used for the classification perform poorly in the identification of these small sub-populations of molecular traces. Robust removal of tunnelling-only traces before clustering is thus of great interest. Neural networks have been proven to be powerful in the classification of data samples with predictable behaviour, but often show large sensitivity to the underlying training data. In this study we report on a neural network method for the separation of tunnelling-only traces in conductance vs. displacement measurements that achieves excellent classification performance for complete and unseen data sets. This method is particularly useful for data sets in which the yield of molecular traces is low or which comprise of a significant number of traces displaying a jump from tunneling features to a molecular plateau. To reference this document use: http://resolver.tudelft.nl/uuid:a8feb77e-7ef1-4278-92f8-78e114b56eb4 DOI https://doi.org/10.1039/D3TC02346J ISSN 2050-7526 Source Journal of Materials Chemistry C: materials for optical and electronic devices, 11 (44), 15564-15570 Part of collection Institutional Repository Document type journal article Rights © 2023 F. van Veen, L. Ornago, H.S.J. van der Zant, M. El Abbassi Files PDF d3tc02346j.pdf 4.72 MB Close viewer /islandora/object/uuid:a8feb77e-7ef1-4278-92f8-78e114b56eb4/datastream/OBJ/view