Advancing vapor-deposited perovskite solar cells <i>via</i> machine learning-Reference-Cited by-同舟云学术

Advancing vapor-deposited perovskite solar cells via machine learning

Published:2023 Issue:25 Volume:11 Page:13201-13208
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Wang Jiazheng¹,Qi Yuchen¹,Zheng Haofeng¹,Wang Ruilong¹,Bai Siyou¹,Liu Yanan¹,Liu Qi¹,Xiao Jin¹,Zou Dechun²^ORCID,Hou Shaocong¹^ORCID

Affiliation:

1. School of Electrical Engineering and Automation, Wuhan University Shenzhen Research Institute, Wuhan University, Wuhan 430072, China

2. Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China

Abstract

The optimal device architecture and its fabrication parameters for vapor-deposited perovskite solar cells are investigated by machine learning, predicting the highest power conversion efficiency of 26.1%.

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/TA/D3TA00027C

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