Estimating single molecule conductance from spontaneous evolution of a molecular contact

Gil, Marie-Eve; Malinowski, T.; Iazykov, Maksym; Klein, Hubert

doi:10.1063/1.5018252

Cited by 8 publications

(13 citation statements)

References 37 publications

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“…The multiple-peak structure for ADT was reproduced by several other groups; the full dataset is available in the Su et al (2015), and Gil et al (2018). The spread in the data is large, but the systematic trends in the separate works are shown by connecting the data points obtained from three works, namely, Ulrich et al (2006), the H peak from , and the H peak from Haiss et al (2009).…”

Section: Alkanedithiolsmentioning

confidence: 99%

“…Histograms that show only a single peak and for which the identification in terms of these three categories is not discussed are shown as gray bullets. The data include published work from Cui et al (2001), Xu and Tao (2003), Xu, Xiao, and Tao (2003), Haiss et al (2004, 2009)Chen et al (2006), González et al (2006), He et al (2006), Jang et al (2006), Li et al (2006, 2008), Sek et al (2006), Ulrich et al (2006), Wierzbinski and Slowinski (2006), Huang et al (2007), Nishikawa et al (2007), Park et al (2007), Hihath et al (2008), Huisman et al (2008), Kiguchi, Sekiguchi, and Murakoshi (2008), Xia, Diez-Perez, and Tao (2008), Song et al (2009, 2010)Martin (2010), Arroyo et al (2011), Kim, Hellmuth et al (2011), Kim, Song et al (2011), Rascón-Ramos et al (2015),Su et al (2015), andGil et al (2018). The spread in the data is large, but the systematic trends in the separate works are shown by connecting the data points obtained from three works, namely,Ulrich et al (2006), the H peak from, and the H peak fromHaiss et al (2009).…”

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confidence: 99%

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Advances and challenges in single-molecule electron transport

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Electronic transport properties of single-molecule junctions have been widely measured by several techniques, including mechanically controllable break junctions, electromigration break junctions, and by means of scanning tunneling microscopes. In parallel, many theoretical tools have been developed and refined for describing such transport properties and for obtaining numerical predictions. Most prominent among these theoretical tools are those based upon density functional theory. In this review, theory and experiment are critically compared, and this confrontation leads to several important conclusions. The theoretically predicted trends nowadays reproduce the experimental findings well for series of molecules with a single well-defined control parameter, such as the length of the molecules. The quantitative agreement between theory and experiment usually is less convincing, however. Two main sources for the quantitative discrepancies can be identified. Experimentally, the atomic structure of the junction typically realized in the measurement is not well known, so simulations rely on plausible scenarios. In theory, correlation effects can be included only in approximations that are difficult to control for experimentally relevant situations. Therefore, one typically expects qualitative agreement with present modeling tools; in exceptional cases a quantitative agreement has already been achieved. For further progress, benchmark systems are required that are sufficiently well defined by experiment to allow quantitative testing of the approximation schemes underlying the theoretical modeling. Several key experiments can be identified suggesting that the present description may even be qualitatively incomplete in some cases. Such key experimental observations and their current models are also discussed here, leading to several suggestions for extensions of the models toward including dynamic image charges, electron correlations, and polaron formation.

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Section: Alkanedithiolsmentioning

confidence: 99%

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confidence: 99%

Advances and challenges in single-molecule electron transport

et al. 2020

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“…Current histograms and single molecule conductance are extracted from these measurements as reported in Ref. 56 and detailed in the supporting information.…”

Section: Mechanically Controlled Break Junction (Mcbj)mentioning

confidence: 99%

“…One of the main benefits of the measurement and analysis method used here, lies in eliminating the constant (or slowly varying) tunneling or ionic contribution to the net conductance. 56 In these conditions, it makes sense to compare conductance values of protonated and neutral molecules. Table 3 gathers the conductance values obtained for neutral and protonated molecules B at Vbias = 35, 70 and 140 mV (see supporting information, figures S14-S16 given the corresponding current traces and histograms of currents and figure S17 for the GMOL vs.…”

Section: Single Molecule Conductance Measurements By Mcbjmentioning

confidence: 99%

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Electrical molecular switch addressed by chemical stimuli

et al. 2020

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We demonstrate that the conductance switching of benzo-bis(imidazole) molecules upon protonation depends on the lateral functional groups. The protonated H-substituted molecule shows a higher conductance than the neutral one (Gpro>Gneu), while the opposite (Gneu>Gpro) is observed for a molecule laterally functionalized by amino-phenyl groups. These results are demonstrated at various scale lengths : self-assembled monolayer, tiny nanodot-molecule junction and single molecules. From ab-initio theoretical calculations, we conclude that for the H-substituted molecule, the result Gpro>Gneu is correctly explained by a reduction of the LUMO-HOMO gap, while for the amino-phenyl functionnalized molecule, the result Gneu>Gpro is consistent with a shift of HOMO, which reduces the density of states at the Fermi energy.

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Unsupervised Segmentation-Based Machine Learning as an Advanced Analysis Tool for Single Molecule Break Junction Data

et al. 2020

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Improved understanding of charge-transport in single molecules is essential for harnessing the potential of molecules, e.g., as circuit components at the ultimate size limit. However, interpretation and analysis of the large, stochastic data sets produced by most quantum transport experiments remain an ongoing challenge to discovering much-needed structure–property relationships. Here, we introduce segment clustering, a novel unsupervised hypothesis generation tool for investigating single molecule break junction distance–conductance traces. In contrast to previous machine learning approaches for single molecule data, segment clustering identifies groupings of similar pieces of traces instead of entire traces. This offers a new and advantageous perspective into data set structure because it facilitates the identification of meaningful local trace behaviors that may otherwise be obscured by random fluctuations over longer distance scales. We illustrate the power and broad applicability of this approach with two case studies that address common challenges encountered in single molecule studies: First, segment clustering is used to extract primary molecular features from a varying background to increase the precision and robustness of conductance measurements, enabling small changes in conductance in response to molecular design to be identified with confidence. Second, segment clustering is applied to a known data mixture to qualitatively separate distinct molecular features in a rigorous and unbiased manner. These examples demonstrate two powerful ways in which segment clustering can aid in the development of structure–property relationships in molecular quantum transport, an outstanding challenge in the field of molecular electronics.

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Estimating single molecule conductance from spontaneous evolution of a molecular contact

Cited by 8 publications

References 37 publications

Advances and challenges in single-molecule electron transport

Advances and challenges in single-molecule electron transport

Electrical molecular switch addressed by chemical stimuli

Unsupervised Segmentation-Based Machine Learning as an Advanced Analysis Tool for Single Molecule Break Junction Data

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