Configuration-Specific Insight into Single-Molecule Conductance and Noise Data Revealed by the Principal Component Projection Method

We explore the merits of neural network boosted, principal-component-projection-based,unsupervised data classification in single-molecule break junctionmeasurements, demonstrating that this method identifies highly relevanttrace classes according to the well-defined and well-visualized internalcorrelations of the data set. To this end, we investigate single-moleculestructures exhibiting double molecular configurations, exploring therole of the leading principal components in the identification ofalternative junction evolution trajectories. We show how the properprincipal component projections can be applied to separately analyzethe high- or low-conductance molecular configurations, which we exploitin 1/f-type noise measurements on bipyridine molecules. This approachuntangles the unclear noise evolution of the entire data set, identifyingthe coupling of the aromatic ring to the electrodes through the pi orbitals in two distinct conductance regions, and its subsequent uncouplingas these configurations are stretched.

Automated summary

We examine the benefits of neural network boosted, principal-component-projection-based, unsupervised data classification in single-molecule break junction measurements, demonstrating that this method can identify highly relevant trace classes based on the well-defined and well-visualized internal correlations of the dataset.