A reference-free clustering method for the analysis of molecular break-junction measurements
A reference-free clustering method for the analysis of molecular break-junction measurements
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DOI：
10.1063/1.5089198
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发表时间：
2019-04-08
影响因子：
4
通讯作者：
Perrin, Mickael L.
中科院分区：
文献类型：
--
作者：
Cabosart, Damien;El Abbassi, Maria;Perrin, Mickael L.
单分子断结测量本质上是随机的，需要获取大量的“断结痕迹”数据集，以深入了解所研究分子的一般电子特性。例如，分子最可能的电导值通常是从这些痕迹构建的电导直方图中提取出来的。在这篇文章中，我们提出了一种无监督和无参考的机器学习工具，以改进机械控制断结（MCBJ）测量中低聚（苯基乙烯基）二硫醇电导的测定。我们的方法允许对基于图像识别技术的单个断裂痕迹进行分类。此外，将该技术应用于多个合并的数据集，可以识别不同样本中存在的常见破坏行为，从而识别全球趋势。特别是，我们发现提取的分子电导的变化可以显着减少，从而从MCBJ数据集中更可靠地估计分子电导值。最后，我们的方法可以更广泛地应用于不同的测量类型，这些测量类型可以转换为二维图像。(C) 2019作者。
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. (C) 2019 Author(s).