Unsupervised Segmentation-Based Machine Learning as an Advanced Analysis Tool for Single Molecule Break Junction Data
Unsupervised Segmentation-Based Machine Learning as an Advanced Analysis Tool for Single Molecule Break Junction Data
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DOI：
10.1021/acs.jpcc.0c03612
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发表时间：
2020-08-20
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.;Ivie, Jeffrey A.;Monti, Oliver L. A.
提高对单分子中电荷输运的理解对于利用分子的潜力是必不可少的，例如，在最终尺寸限制下作为电路元件。然而，对大多数量子输运实验产生的大型随机数据集的解释和分析仍然是发现急需的结构-性质关系的持续挑战。在这里，我们引入了片段聚类，这是一种新的无监督假设生成工具，用于研究单分子断裂结距离-电导轨迹。与之前针对单分子数据的机器学习方法相比，片段聚类识别相似痕迹片段的分组，而不是整个痕迹。这为数据集结构提供了一个新的和有利的视角，因为它有助于识别有意义的局部跟踪行为，否则这些行为可能会被更远距离尺度上的随机波动所掩盖。我们通过两个案例研究说明了这种方法的力量和广泛的适用性，这些案例研究解决了单分子研究中遇到的常见挑战：首先，片段聚类用于从不同背景中提取主要分子特征，以提高电导测量的精度和鲁棒性，使电导响应分子设计的微小变化能够被自信地识别出来。其次，将片段聚类应用于已知的数据混合物，以严格和无偏的方式定性地分离不同的分子特征。这些例子展示了两种强有力的方式，片段聚类可以帮助发展分子量子输运中的结构-性质关系，这是分子电子学领域的一个突出挑战。
Improved understanding of charge-transport in single molecules is essential for harnessing the potential of molecules, e.g., as circuit components at the ultimate size limit. However, interpretation and analysis of the large, stochastic data sets produced by most quantum transport experiments remain an ongoing challenge to discovering much-needed structure-property relationships. Here, we introduce segment clustering, a novel unsupervised hypothesis generation tool for investigating single molecule break junction distance-conductance traces. In contrast to previous machine learning approaches for single molecule data, segment clustering identifies groupings of similar pieces of traces instead of entire traces. This offers a new and advantageous perspective into data set structure because it facilitates the identification of meaningful local trace behaviors that may otherwise be obscured by random fluctuations over longer distance scales. We illustrate the power and broad applicability of this approach with two case studies that address common challenges encountered in single molecule studies: First, segment clustering is used to extract primary molecular features from a varying background to increase the precision and robustness of conductance measurements, enabling small changes in conductance in response to molecular design to be identified with confidence. Second, segment clustering is applied to a known data mixture to qualitatively separate distinct molecular features in a rigorous and unbiased manner. These examples demonstrate two powerful ways in which segment clustering can aid in the development of structure-property relationships in molecular quantum transport, an outstanding challenge in the field of molecular electronics.