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Rapid Computational Discovery of Pi-Conjugated Materials

Kanal, Ilana Yocheved (2017) Rapid Computational Discovery of Pi-Conjugated Materials. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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The focus of this thesis is conjugated polymer properties for improved computational discovery of $\pi$-conjugated materials. Combination of these materials in differing orders alter the electronic structure and in tetramers, on average, an energy effect is seen. When expanded to hexamers, it became apparent that a more complicated effect exists that depends on block length and placement of that block or sequence within a hexamer. Sequence effects were applied both computationally and experimentally for combinations of benzothiadiazole and phenylene vinylene monomers to confirm importance of sequence in both solar cell performance as sequence can affect intrinsic and bulk properties orthogonally, such as HOMO-LUMO gap.

In addition to sequence study, inverse design of conjugated polymers from computed electronic structure properties demonstrate that while it is unreliable to predict polymer properties from the monomer properties alone, it is very reliable to make predictions from simple models. These models allow for better polymer property predictions without costly polymer calculations.

A large scale computational investigation assessing the utility of common classical force fields for computational screening of low energy conformers provided us with insight for the most reliable methods to use when screening molecules. Using statistical analyses on the energies of up to 250 diverse conformers of 700 different molecular structures, we find that energies and geometries from widely-used classical force fields show poor energy correlation with semiempirical and DFT energies calculated at PM7 geometries. In contrast, semiempirical (PM7) energies show better correlation with DFT calculations. With these results, we make recommendations for more reliably carrying out conformer screening.

Sequence effect, models for polymer predictions and assessment of classical force field methods for low energy conformer predictions are combined to produce our genetic algorithm to rapidly, computationally select materials. Optimization of our genetic algorithm shows that with relatively few calculations, millions of molecules can be screened with a significant speedup compared with brute force calculation of those same molecules.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Kanal, Ilana Yochevediek2@pitt.eduiek2
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairHutchison, Geoffreygeoffh@pitt.edugeoffh
Committee MemberLambrecht, Daniellambrecht@pitt.edulambrecht
Committee MemberMeyer, Taratara.meyer@pitt.edutara.meyer
Committee MemberKeith, JohnJAKEITH@pitt.eduJAKEITH
Date: 24 September 2017
Date Type: Publication
Defense Date: 30 March 2017
Approval Date: 24 September 2017
Submission Date: 20 April 2017
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 262
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: Computational Chemistry, Physical Chemistry
Date Deposited: 24 Sep 2017 21:33
Last Modified: 24 Sep 2018 05:15


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