Machine learning‐assisted performance analysis of organic photovoltaics

Sijing Zhong; Jiayi Huang; Hengyu Meng; Zhuo Feng; Qianyue Wang; Zhenyu Huang; Lijie Zhang; Shiwei Li; Weiyang Gong; Yusen Huang; Lei Ying; Ning Li

doi:10.1002/mgea.74

Materials Genome Engineering Advances (Dec 2024)

Machine learning‐assisted performance analysis of organic photovoltaics

Sijing Zhong,
Jiayi Huang,
Hengyu Meng,
Zhuo Feng,
Qianyue Wang,
Zhenyu Huang,
Lijie Zhang,
Shiwei Li,
Weiyang Gong,
Yusen Huang,
Lei Ying,
Ning Li

Affiliations

Sijing Zhong: Institute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou China
Jiayi Huang: Institute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou China
Hengyu Meng: Institute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou China
Zhuo Feng: Institute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou China
Qianyue Wang: Institute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou China
Zhenyu Huang: Institute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou China
Lijie Zhang: Institute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou China
Shiwei Li: Institute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou China
Weiyang Gong: Institute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou China
Yusen Huang: Institute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou China
Lei Ying: Institute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou China
Ning Li: Institute of Polymer Optoelectronic Materials and Devices Guangdong Basic Research Center of Excellence for Energy and Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices South China University of Technology Guangzhou China

DOI: https://doi.org/10.1002/mgea.74
Journal volume & issue: Vol. 2, no. 4
pp. n/a – n/a

Abstract

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Abstract Although the power conversion efficiency of organic solar cells (OSCs) has been rapidly improved, there is still a lot of room for designing and developing new materials and their combinations to approach the efficiency limit. In this work, we establish a database of ∼100 bulk heterojunction OSCs composed of representative donors and acceptors reported in the literature, and train machine learning models to identify the efficiency potential of donor‐acceptor combinations. We find that the fully connected neural network achieves a Pearson coefficient of up to 0.88 for predicting the efficiency of OSCs with different combinations of donors and acceptors. We use sure independence screening and sparsifying method with feature analysis to analyze and evaluate the performance of OSCs. To prove the reliability and viability of the predictive model, we introduce the theoretical efficiency limits and confidence tests into the process, which provides a simple but reliable solution to quickly analyze and evaluate the potential of OSC materials and material combinations.

Published in Materials Genome Engineering Advances

ISSN: 2940-9489 (Print); 2940-9497 (Online)
Publisher: Wiley-VCH
Country of publisher: Germany
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials; Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware; Technology: Technology (General)
Website: https://onlinelibrary.wiley.com/journal/29409497

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