Grid-Based Correlation Analysis to Identify Rare Quantum Transport Behaviors

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.