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Accelerated Computational Discovery and Evaluation of Conjugated Organic Materials

Elsey, Danielle (2023) Accelerated Computational Discovery and Evaluation of Conjugated Organic Materials. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Molecular discovery plays an undeniably important role in technological advancement. The ability to create new materials with properties tailored to particular applications is incredibly powerful. In the ever changing world of electronics, such materials allow for breakthroughs in size, speed, time, and efficiency of the components which make up the machines that drive the modern world. One particularly influential class of materials in this realm is conjugated organic molecules, whose unique extended $\pi$-systems make them ideal for many electronic applications. Given the immense size of chemical space, finding and evaluating useful novel molecular structures can be time consuming and difficult, however, so efficient computational tools are vital to this technological progress.

In this work, we seek to search for and evaluate novel conjugated organic molecules through genetic algorithm (GA) development and chemical calculation method testing. As a proven method for efficiently searching chemical space, a GA is implemented and used to search for hexamer candidates with simultaneously high polarizabilities and dipole moments for high dielectric applications. These candidates are examined and conclusions are drawn about the connection between their molecular structure and energetic properties. Because quality polarizability calculations are crucial for effective evaluation of conjugated materials, a benchmark study is performed on several computational calculation methods to assess their general accuracy and efficiency, as well as their suitability for use on highly polarizable extended conjugated systems. This leads to both finding an important systematic error in the commonly used GFN2-xTB/D4 method, as well as a linear correction to DFT results that allows for the accuracy of augmented basis sets to be had at a much lower computational cost. Turning our attention to method development and improvement, we investigate best practices for using GAs for molecular discovery. A convergence method that allows for self-termination, along with tuned values for numerous hyperparameters are determined. Finally, strategies for accelerating molecular discovery GAs are investigated, including initial work on developing a shinking gene pool protocol.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Elsey, Danielledch45@pitt.edudch450000-0001-7600-847X
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairHutchison,
Committee MemberJordan,
Committee MemberLaaser,
Committee MemberWilmer,
Date: 10 May 2023
Date Type: Publication
Defense Date: 23 March 2023
Approval Date: 10 May 2023
Submission Date: 22 March 2023
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 188
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Chemistry
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: genetic algorithm, chemical discovery, materials discovery, molecular discovery, dielectric materials
Date Deposited: 10 May 2023 18:34
Last Modified: 10 May 2024 05:15

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