Configuration-Specific Insight into Single-Molecule Conductance and Noise Data Revealed by the Principal Component Projection Method,The Journal of Physical Chemistry Letters

当前位置： X-MOL 学术 › J. Phys. Chem. Lett. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Configuration-Specific Insight into Single-Molecule Conductance and Noise Data Revealed by the Principal Component Projection Method
The Journal of Physical Chemistry Letters ( IF 4.8 ) Pub Date : 2023-05-30 , DOI: 10.1021/acs.jpclett.3c00677
Z Balogh _{1,

2} , G Mezei _{1,

2} , N Tenk ₁ , A Magyarkuti ₁ , A Halbritter _{1,

2}

Affiliation

We explore the merits of neural network boosted, principal-component-projection-based, unsupervised data classification in single-molecule break junction measurements, demonstrating that this method identifies highly relevant trace classes according to the well-defined and well-visualized internal correlations of the data set. To this end, we investigate single-molecule structures exhibiting double molecular configurations, exploring the role of the leading principal components in the identification of alternative junction evolution trajectories. We show how the proper principal component projections can be applied to separately analyze the high- or low-conductance molecular configurations, which we exploit in 1/f-type noise measurements on bipyridine molecules. This approach untangles the unclear noise evolution of the entire data set, identifying the coupling of the aromatic ring to the electrodes through the π orbitals in two distinct conductance regions, and its subsequent uncoupling as these configurations are stretched.

中文翻译：

主成分投影法揭示的单分子电导和噪声数据的特定配置洞察

我们探索了神经网络增强的、基于主成分投影的、无监督的数据分类在单分子断裂连接测量中的优点，证明了该方法根据明确定义和可视化的内部相关性识别高度相关的痕迹类数据集。为此，我们研究了表现出双分子构型的单分子结构，探索了主要主要成分在识别替代连接演化轨迹中的作用。我们展示了如何应用适当的主成分投影来分别分析高电导或低电导分子构型，我们在联吡啶分子的 1/f 型噪声测量中利用了这些构型。这种方法解开了整个数据集不清晰的噪声演化，

更新日期：2023-05-30

点击分享查看原文

点击收藏

公开下载

阅读更多本刊最新论文本刊介绍/投稿指南11