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Computational Approach Toward Rational Device Engineering of Organic Photovoltaics.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational Approach Toward Rational Device Engineering of Organic Photovoltaics./
作者:	Lee, Franklin L.
面頁冊數:	1 online resource (156 pages)
附註:	Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
Contained By:	Dissertations Abstracts International82-02B.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28114689click for full text (PQDT)
ISBN:	9798662538948

Computational Approach Toward Rational Device Engineering of Organic Photovoltaics.
Lee, Franklin L.

Computational Approach Toward Rational Device Engineering of Organic Photovoltaics. - 1 online resource (156 pages)

Source: Dissertations Abstracts International, Volume: 82-02, Section: B.

Thesis (Ph.D.)--Stanford University, 2018.

Includes bibliographical references

Organic photovoltaics (OPVs) have emerged as a promising alternative to conventional PV technology due to their low cost and industry-level scalability with high-volume production through solution-based processing. OPVs combine the unique flexibility and versatility of plastics with electronic properties, making them amenable to applications in the "Internet of Things" and distributed generation applications. The current key challenge for wide adaptation of OPVs is the lack of high power conversion efficiency (PCE) in large scale roll-to-roll processed devices. A key factor is the morphology: there exists disorder between the electron donor and electron acceptor materials in the active layer, and the mechanisms by which the morphology can be tuned are not well understood. Simulation is a promising inexpensive technique for exploring OPVs in the large parameter space of both processing methods and chemical components. In this work, we leverage and improve upon these computational approaches to reduce the need for iterative design for OPVs. First, we develop a multiscale molecular dynamics (MD) model to provide understanding of morphology evolution during solution processing. In addition, we train and utilize a predictive deep learning model to study the correlation of performance with the chemical and engineering design considerations. These parallel approaches allow for an accelerated sampling of the parameter space of OPV conditions, which in turn leads to targeted experiments.In the multiscale physical simulation approach, we first use all-atom MD to understand the behavior of an isoindigo-based donor-acceptor conjugated polymer in solution. The parametrization of the force field and the calculation of properties for different polymer lengths and environments are described. These properties are then matched to parametrize a 2D coarse-grained (CG) model for MD simulations of systems of polymer, fullerene, and solvent. The CG model is then applied to calculate morphological properties, such as fullerene aggregation and donor and acceptor domain sizes, during various stages of the evaporation process. In the deep learning approach, we first construct a machine-readable dataset of OPV information, comprised purely of experimental data from the literature. Next, we explain fundamental machine learning and deep learning concepts and their transferability to the problem of OPV optimization, particularly graph convolution neural networks (GCNNs). Using the BRICS algorithm, we generate a large space of donor molecules, many of which have not been reported previously in the literature. We use the GCNN model to evaluate the performance metrics of prospective combinations of donor, acceptor, solvent, and donor/acceptor ratio and provide recommendations for those parameters to experimental collaborators. Throughout these steps, we contrast the performance of the GCNN with a simple random forest model and rationalize the benefit of the deep learning paradigm. Finally, an outlook is provided for future ideas and challenges in the application of deep learning to various materials science problems.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798662538948Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

PhotovoltaicsIndex Terms--Genre/Form:

542853
Electronic books.

Computational Approach Toward Rational Device Engineering of Organic Photovoltaics.
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502 $a Thesis (Ph.D.)--Stanford University, 2018.
504 $a Includes bibliographical references
520 $a Organic photovoltaics (OPVs) have emerged as a promising alternative to conventional PV technology due to their low cost and industry-level scalability with high-volume production through solution-based processing. OPVs combine the unique flexibility and versatility of plastics with electronic properties, making them amenable to applications in the "Internet of Things" and distributed generation applications. The current key challenge for wide adaptation of OPVs is the lack of high power conversion efficiency (PCE) in large scale roll-to-roll processed devices. A key factor is the morphology: there exists disorder between the electron donor and electron acceptor materials in the active layer, and the mechanisms by which the morphology can be tuned are not well understood. Simulation is a promising inexpensive technique for exploring OPVs in the large parameter space of both processing methods and chemical components. In this work, we leverage and improve upon these computational approaches to reduce the need for iterative design for OPVs. First, we develop a multiscale molecular dynamics (MD) model to provide understanding of morphology evolution during solution processing. In addition, we train and utilize a predictive deep learning model to study the correlation of performance with the chemical and engineering design considerations. These parallel approaches allow for an accelerated sampling of the parameter space of OPV conditions, which in turn leads to targeted experiments.In the multiscale physical simulation approach, we first use all-atom MD to understand the behavior of an isoindigo-based donor-acceptor conjugated polymer in solution. The parametrization of the force field and the calculation of properties for different polymer lengths and environments are described. These properties are then matched to parametrize a 2D coarse-grained (CG) model for MD simulations of systems of polymer, fullerene, and solvent. The CG model is then applied to calculate morphological properties, such as fullerene aggregation and donor and acceptor domain sizes, during various stages of the evaporation process. In the deep learning approach, we first construct a machine-readable dataset of OPV information, comprised purely of experimental data from the literature. Next, we explain fundamental machine learning and deep learning concepts and their transferability to the problem of OPV optimization, particularly graph convolution neural networks (GCNNs). Using the BRICS algorithm, we generate a large space of donor molecules, many of which have not been reported previously in the literature. We use the GCNN model to evaluate the performance metrics of prospective combinations of donor, acceptor, solvent, and donor/acceptor ratio and provide recommendations for those parameters to experimental collaborators. Throughout these steps, we contrast the performance of the GCNN with a simple random forest model and rationalize the benefit of the deep learning paradigm. Finally, an outlook is provided for future ideas and challenges in the application of deep learning to various materials science problems.
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