Structure-Function Relationships and Advanced Data Analysis in Single Molecule Quantum Transport
AdvisorMonti, Oliver L.A.
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PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractIncorporating individual small organic molecules into electronic circuits has the potential to enable smaller and more efficient devices, while also providing an excellent experimental platform for investigating the fundamental physics and chemistry of quantum transport. Key to advancing both of these goals is the continued development of structure-function relationships that predictively connect molecular design to observed transport behavior. Despite their apparent simplicity, single-molecule systems often display complex interactions between different physical effects, and so structure-function relationships that account for these interconnections are an especially important, and relatively understudied, need for the field. A second major challenge for single-molecule transport research is that modern experimental platforms tend to produce large, stochastic, and high-dimensional datasets. Methods to robustly extract meaningful information from such datasets are thus required to fully probe the range of behaviors occurring in single-molecule circuits, and to understand how those behaviors relate back to molecular design. In this dissertation, I describe contributions to help address the need for both nuanced structure-function relationships and sophisticated data analysis strategies for single-molecule quantum transport research. The experimental platform I used to measure single-molecule charge transport is described in detail, along with the type of data it collects and the subtleties of how those data are processed. Motivated by those details, I describe my overall approach to analyzing single-molecule data and then introduce, validate, and utilize novel machine learning algorithms that I developed to address specific challenges. These include a novel segment clustering algorithm for reliably extracting molecular features and an original correlation-based framework for identifying meaningful rare events. Using some of these new tools, I then report single-molecule conductance measurements for two series of molecules that reveal previously unknown connections between different physical effects in metal/single-molecule/metal junctions. The first study focuses on energy-level alignment between the bridging molecule and the metal electrodes, and finds that linked effects determine the tunability of conductance for molecules with varying chemical substituents. Finally, in the second study I demonstrate how backbone conformation and metal/molecule electronic coupling, which are often approximated as independent, can in fact be strongly correlated in the case of fairly common structural components. Together, all of these advances in the collection, analysis, and interpretation of single-molecule transport data help to deepen our understanding of physical chemistry in nanoscopic systems.
Degree ProgramGraduate College