Grid-Based Correlation Analysis to Identify Rare Quantum Transport Behaviors
Grid-Based Correlation Analysis to Identify Rare Quantum Transport Behaviors
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DOI：
10.1021/acs.jpcc.1c04794
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发表时间：
2021-08-17
影响因子：
3.7
通讯作者：
Monti, Oliver L. A.
中科院分区：
文献类型：
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作者：
Bamberger, Nathan D.;Dyer, Dylan;Monti, Oliver L. A.
大多数单分子运输实验都会产生包含多种行为的大型随机数据集，这既对他们的分析提出了挑战，也为发现新的物理见解提供了机会。最近，已经开发了几种无监督聚类算法来帮助从单分子传输数据中提取和分离不同的特征。然而，这些聚类方法主要被设计和用于提取主要数据集组件，因此可能难以识别非常罕见的特征和行为，但这些特征和行为可能包含物理上有意义的信息。因此，在这项工作中，我们引入了一种全新的分析框架，专门用于单分子断裂连接数据中的罕见事件检测，以帮助解锁此类信息，并提供与聚类不同的隐式假设的新视角。我们的方法利用断裂痕迹与其自身历史的相关性概念来稳健地识别与可再现的罕见行为相对应的距离电导空间的路径。作为一个示范性且重要的例子，我们重点关注短分子的罕见电导平台，在检查原始数据时，这些平台基本上是不可见的。我们证明，我们的基于网格的相关工具可以成功且可重复地在真实实验数据集中定位这些罕见的平台，包括在传统聚类方法发现具有挑战性的情况下。这一结果使得未来能够考虑更广泛的分子，并表明我们的新方法是一种有用的工具，可以更广泛地检测单分子传输数据中罕见但有意义的行为。
Most single-molecule transport experiments produce large and stochastic data sets containing a wide range of behaviors, presenting both a challenge to their analysis and an opportunity for discovering new physical insights. Recently, several unsupervised clustering algorithms have been developed to help extract and separate distinct features from single-molecule transport data. However, these clustering approaches have been primarily designed and used to extract major data set components and are consequently likely to struggle with identifying very rare features and behaviors that may nonetheless contain physically meaningful information. In this work, we thus introduce a completely new analysis framework specifically designed for rare event detection in single-molecule break junction data to help unlock such information and provide a new perspective with different implicit assumptions than clustering. Our approach leverages the concept of correlations of breaking traces with their own history to robustly identify paths through distance-conductance space that correspond to reproducible rare behaviors. As both a demonstrative and important example, we focus on rare conductance plateaus for short molecules, which can be essentially invisible when examining raw data. We show that our grid-based correlation tools successfully and reproducibly locate these rare plateaus in real experimental data sets, including in situations that traditional clustering approaches find challenging. This result enables a broader variety of molecules to be considered in the future and suggests that our new approach is a useful tool for detecting rare-yet-meaningful behaviors in single-molecule transport data more generally.