Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning
Author
Source
International Journal of Polymer Science
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-03-31
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Chen, Fang-Chung. 2019. Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning. International Journal of Polymer Science،Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1168026
Modern Language Association (MLA)
Chen, Fang-Chung. Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning. International Journal of Polymer Science No. 2019 (2019), pp.1-7.
https://search.emarefa.net/detail/BIM-1168026
American Medical Association (AMA)
Chen, Fang-Chung. Virtual Screening of Conjugated Polymers for Organic Photovoltaic Devices Using Support Vector Machines and Ensemble Learning. International Journal of Polymer Science. 2019. Vol. 2019, no. 2019, pp.1-7.
https://search.emarefa.net/detail/BIM-1168026
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1168026