arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.10731cond-mat.mes-hall
收藏

基于无监督分割的机器学习作为单分子断裂连接数据的高级分析工具

Unsupervised Segmentation-Based Machine Learning as an Advanced Analysis Tool for Single Molecule Break Junction Data

Nathan D. Bamberger, Jeffrey A. Ivie, Keshaba N. Parida, Dominic V. McGrath, Oliver L. A. Monti

摘要:

提高对单分子中电荷传输的理解对于利用分子的潜力至关重要,例如,作为最终尺寸限制的电路组件。然而,大多数量子传输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,我们介绍段聚类,一种新的无监督的假设生成工具,用于调查单分子断裂结距离电导痕迹。与以前的单分子数据机器学习方法相比,分段聚类识别相似的轨迹片段而不是整个轨迹的分组。这为数据集结构提供了一个新的有利视角,因为它有助于识别有意义的局部轨迹行为,否则这些行为可能会被较长距离尺度上的随机波动所掩盖。我们用两个案例研究来说明这种方法的强大性和广泛的适用性,这些案例研究解决了单分子研究中遇到的常见挑战:首先,分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,从而使电导响应分子设计的微小变化能够有信心地识别。其次,分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大的方式,其中片段聚类可以帮助发展分子量子传输中的结构-性质关系,这是分子电子学领域的一个突出挑战。

英文摘要:

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 datasets produced by most quantum transport experiments remains 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 dataset 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑