Grid-Based Correlation Analysis to Identify Rare Quantum Transport Behaviors

• Main Authors: Bamberger, Nathan D, Dyer, Dylan, Parida, Keshaba N, McGrath, Dominic V, Monti, Oliver L. A
• Format: Journal Article
• Language: English
• Published: 17-08-2021
• Subjects: Physics - Data Analysis, Statistics and Probability; Physics - Mesoscale and Nanoscale Physics
• DOI: 10.48550/arxiv.2105.13521

Journal of Physical Chemistry C, 2021 Most single-molecule transport experiments produce large and stochastic
datasets containing a wide range of behaviors, presenting both a challenge to
their analysis, but also 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 dataset components, and are consequently likely to
struggle with identifying very rare features and behaviors which 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 datasets,
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.