Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning

Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning

Herein, we report virtual screening of potential semiconductor polymers for high-performance organic photovoltaic (OPV) devices using various machine learning algorithms. We particularly focus on support vector machine (SVM) and ensemble learning approaches. We found that the power conversion effici...

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Bibliographic Details
Main Author: Fang-Chung Chen
Format: Article
Language:English
Published: Hindawi Limited 2019-01-01
Series:International Journal of Polymer Science
Online Access:http://dx.doi.org/10.1155/2019/4538514
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spelling doaj-2376d6153d8947f38c619ce443bd461a2020-11-24T21:32:29ZengHindawi LimitedInternational Journal of Polymer Science1687-94221687-94302019-01-01201910.1155/2019/45385144538514Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble LearningFang-Chung Chen0Department of Photonics, National Chiao Tung University, Hsinchu 30010, TaiwanHerein, we report virtual screening of potential semiconductor polymers for high-performance organic photovoltaic (OPV) devices using various machine learning algorithms. We particularly focus on support vector machine (SVM) and ensemble learning approaches. We found that the power conversion efficiencies of the device prepared with the polymer candidates can be predicted with their structure fingerprints as the only inputs. In other words, no preliminary knowledge about material properties was required. Additionally, the predictive performance could be further improved by “blending” the results of the SVM and random forest models. The resulting ensemble learning algorithm might open up a new opportunity for more precise, high-throughput virtual screening of conjugated polymers for OPV devices.http://dx.doi.org/10.1155/2019/4538514
collection DOAJ
language English
format Article
sources DOAJ
author Fang-Chung Chen
spellingShingle Fang-Chung Chen
Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning
International Journal of Polymer Science
author_facet Fang-Chung Chen
author_sort Fang-Chung Chen
title Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning
title_short Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning
title_full Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning
title_fullStr Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning
title_full_unstemmed Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning
title_sort virtual screening of conjugated polymers for organic photovoltaic devices using support vector machines and ensemble learning
publisher Hindawi Limited
series International Journal of Polymer Science
issn 1687-9422
1687-9430
publishDate 2019-01-01
description Herein, we report virtual screening of potential semiconductor polymers for high-performance organic photovoltaic (OPV) devices using various machine learning algorithms. We particularly focus on support vector machine (SVM) and ensemble learning approaches. We found that the power conversion efficiencies of the device prepared with the polymer candidates can be predicted with their structure fingerprints as the only inputs. In other words, no preliminary knowledge about material properties was required. Additionally, the predictive performance could be further improved by “blending” the results of the SVM and random forest models. The resulting ensemble learning algorithm might open up a new opportunity for more precise, high-throughput virtual screening of conjugated polymers for OPV devices.
url http://dx.doi.org/10.1155/2019/4538514
work_keys_str_mv AT fangchungchen virtualscreeningofconjugatedpolymersfororganicphotovoltaicdevicesusingsupportvectormachinesandensemblelearning
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