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.