Fast Dimension-based Partitioning and Merging clustering algorithm

Ghanem, Tamer F.; Elkilani, Wail S.; Abdelkader, Hatem; Hadhoud, Mohiy M.

doi:10.1016/j.asoc.2015.05.049

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…Here, we propose a nonparametric hybrid machine learning approach (Figure ; see also Supporting Information Figures S1–S4) for straightforwardly distinguishing time-evolved single molecular conductance behavior. This is a two-step analysis examining prescreening through a denoised classification via a grid-based DBSCAN (density-based clustering) algorithm − and subsequent rebuilding of histograms from the groups of classified traces. There is only very limited space for adding researcher-crafted parameters in the classification processes.…”

Section: Introductionmentioning

confidence: 99%

Dissecting Time-Evolved Conductance Behavior of Single Molecule Junctions by Nonparametric Machine Learning

Liu

Murayama

Komoto

et al. 2020

J. Phys. Chem. Lett.

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Improved understanding of charge transport in single molecules is essential for utilizing their potential as circuit components at the nanosize limit. However, reliable analyses of varying tunneling current acquired by break junction experiments remain an ongoing challenge to find molecular feature structure−property relationships. In this work, we report on an unsupervised learning approach for investigating molecular signatures in conductance traces. Our hybrid machine learning algorithm compares grids of data in conductance−time domains and judges the similarity without any researcher-crafted parameters to identify fine molecular components that may otherwise be obscured by background fluctuations. We demonstrate its ability for classifying Au−alkanedithiol−Au conductance traces acquired with microfabricated mechanically controllable break junctions. The unbiased procedure was able to not only judge the presence or absence of the carbon chains in the electrode gap but also to identify multiple conductance states of the molecular tunneling junctions with different conformations. This finding may offer a useful tool for studying single-molecule properties using break junction methods.

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Section: Introductionmentioning

confidence: 99%