Opportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells-Reference-Cited by-同舟云学术

Opportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells

Published:2022 Issue:47 Volume:10 Page:17781-17811
ISSN:2050-7526
Container-title:Journal of Materials Chemistry C
language:en
Short-container-title:J. Mater. Chem. C

Author:

Malhotra Prateek¹^ORCID,Khandelwal Kanupriya¹^ORCID,Biswas Subhayan¹,Chen Fang-Chung²³^ORCID,Sharma Ganesh D.¹⁴^ORCID

Affiliation:

1. Department of Physics, The LNM Institute of Information Technology, Jamdoli, Jaipur, 302031, Rajasthan, India

2. Department of Photonics, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan

3. Center for Emergent Functional Matter Science, National Yang Ming Chiao Tung University, Hsinchu, 30010, Taiwan

4. Department of Electronics and Communication Engineering, The LNM Institute of Information Technology, Jamdoli, Jaipur, 302031, Rajasthan, India

Abstract

In this review, current progress, opportunities, and challenges for ML in OSCs have been identified. Given the rapid advances in this field, impactful techniques that have been useful in extracting meaningful insights are discussed.

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/TC/D2TC03276G

Reference142 articles.

1. Device Performance of Emerging Photovoltaic Materials (Version 2)

2. The Path to 20% Power Conversion Efficiencies in Nonfullerene Acceptor Organic Solar Cells

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