Department of Chemistry, Columbia University, New York, New York 10027, United States.
Shanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power, Shanghai University of Electric Power, Shanghai 200090, China.
Nano Lett. 2020 May 13;20(5):3320-3325. doi: 10.1021/acs.nanolett.0c00198. Epub 2020 Apr 9.
The scanning tunneling microscope-based break junction (STM-BJ) is used widely to create and characterize single metal-molecule-metal junctions. In this technique, conductance is continuously recorded as a metal point contact is broken in a solution of molecules. Conductance plateaus are seen when stable molecular junctions are formed. Typically, thousands of junctions are created and measured, yielding thousands of distinct conductance versus extension traces. However, such traces are rarely analyzed individually to recognize the types of junctions formed. Here, we present a deep learning-based method to identify molecular junctions and show that it performs better than several commonly used and recently reported techniques. We demonstrate molecular junction identification from mixed solution measurements with accuracies as high as 97%. We also apply this model to an electric field-driven isomerization reaction of a [3]cumulene to follow the reaction over time. Furthermore, we demonstrate that our model can remain accurate even when a key parameter, the average junction conductance, is eliminated from the analysis, showing that our model goes beyond conventional analysis in existing methods.
基于扫描隧道显微镜的断键（STM-BJ）广泛用于创建和表征单金属分子金属结。在该技术中，当在分子溶液中形成稳定的分子结时，连续记录电导。当形成稳定的分子结时，会出现电导平台。通常，会创建和测量数千个结，从而产生数千个不同的电导与延伸轨迹。然而，很少对这些轨迹进行单独分析以识别形成的结的类型。在这里，我们提出了一种基于深度学习的方法来识别分子结，并表明它比几种常用的和最近报道的技术表现更好。我们证明了从混合溶液测量中识别分子结的方法，其准确率高达 97%。我们还应用该模型来跟踪[3]cumulene在电场驱动下的异构化反应随时间的变化。此外，我们证明即使从分析中消除了关键参数（平均结电导），我们的模型仍然准确，这表明我们的模型超越了现有方法中常规分析的范围。
