Greenstein, Brianna Lauren
(2024)
Molecular Discovery of Materials for Non-Fullerene Based Organic Solar Cells.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Organic solar cells (OSC) are an emerging photovoltaic technology that uses n-type and p-type organic materials to split excitons into free charges. The n-type material, known as the acceptor, and the p-type material, known as the donor, require interdependent chemical properties such as energy-level alignment of their frontier orbitals and complementary absorption spectra to result in a high power conversion efficiency (PCE). Due to the complexity of finding compatible materials, computational guidance is crucial to quickly discover new OSC materials and uncover their molecular design rules. In the first part of this work, a PCE prediction model is developed that can work on non-fullerene acceptor (NFA)-based OSCs with much higher accuracy than previously published models. This model was then used as the fitness function in a genetic algorithm (GA) to design new unfused NFAs. However, before using the GA, we performed a study to optimize the GA hyperparameters such as population size, elitism percentage, selection method, mutation rate, and convergence criteria for molecular discovery. The results of this study provided a set of best practices for the use of GAs for inverse molecular design that can be generalized over multiple chemical properties. Next, the GA was used to discover more than 1,087 unfused NFAs with a predicted PCE above 18%. In the fourth part of this work, we used a series of genetic algorithms and machine learning to find the best combination of materials for tandem OSCs. The PCE prediction model was improved by training on a larger OSC dataset and used as the fitness function to design better NFAs and compatible donors to those NFAs. The top pairs were used as the active layer in a subcell, while a new GA was used to find compatible NFAs and donors for the other subcell in a tandem device that maximized absorption while minimizing spectral overlap. Lastly, we created the largest OSC dataset available and leveraged its unique size to learn more about the molecular design of NFAs and polymer donors. The results of this meta-analysis can guide chemists on the design of new OSC materials.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
27 August 2024 |
| Date Type: |
Publication |
| Defense Date: |
31 May 2024 |
| Approval Date: |
27 August 2024 |
| Submission Date: |
3 June 2024 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
322 |
| 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: |
organic solar cells, machine learning, genetic algorithms, non-fullerene acceptors, molecular discovery |
| Date Deposited: |
27 Aug 2024 14:21 |
| Last Modified: |
27 Aug 2024 14:21 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/46457 |
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