Author ORCID Identifier
Date Available
4-28-2024
Year of Publication
2023
Document Type
Doctoral Dissertation
Degree Name
Doctor of Philosophy (PhD)
College
Arts and Sciences
Department/School/Program
Chemistry
Advisor
Dr. Chad Risko
Abstract
Organic semiconductors have gained widespread attention due to their potential applications in flexible, low-cost, lightweight electronics, energy storage and generation technologies, and sensing applications. However, developing new organic semiconductors with improved performance remains a significant challenge due to the vast chemical space of possible molecular and materials structures. Furthermore, the high cost and time-consuming nature of experimental synthesis and characterization hinder the rapid discovery of new materials. To overcome these challenges, this dissertation presents a data-driven approach to organic semiconductor discovery. The primary focus of this work is the development of data-driven tools, namely machine learning models, to predict critical electronic and structural properties of molecular organic semiconductors. These models are trained on a large dataset of quantum-chemical calculations, enabling the efficient screening of thousands of candidate molecules. In addition to developing the predictive models, this work includes creating a user-friendly web platform. The platform enables access to the models and rapid exploration of the chemical space to design new materials. The platform also includes visualization and analysis tools to guide the design process and facilitate research collaboration. The data-driven tools developed in this research have the potential to significantly accelerate the discovery and development of new molecular organic semiconductors, paving the way for the next generation of flexible electronics and renewable energy technologies. Overall, this dissertation offers a practical and innovative framework for designing organic semiconductors, leveraging data-driven approaches to overcome the challenges of the traditional experimental trial-and-error process.
Digital Object Identifier (DOI)
https://doi.org/10.13023/etd.2023.084
Funding Information
This study was supported by National Science Foundation through the Designing Materials to Revolutionize and Engineer our Future (NSF DMREF) program under award number DMR-1627428
Recommended Citation
Bhat, Vinayak, "Developing and Deploying Data-Driven Tools for Accelerated Design of Organic Semiconductors" (2023). Theses and Dissertations--Chemistry. 178.
https://uknowledge.uky.edu/chemistry_etds/178
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