Tandem solar cells efficiency prediction and optimization via deep learning-Reference-Cited by-同舟云学术

Tandem solar cells efficiency prediction and optimization via deep learning

Published:2021 Issue:4 Volume:23 Page:2991-2998
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Yi Chuqiao¹²³⁴^ORCID,Wu Yuliang⁵⁶⁷⁴^ORCID,Gao Yayu¹²³⁴^ORCID,Du Qingguo⁵⁶⁷⁴^ORCID

Affiliation:

1. School of Electronic Information and Communications

2. Huazhong University of Science and Technology

3. Wuhan 430074

4. China

5. School of Information Engineering

6. Wuhan University of Technology

7. Wuhan 430070

Abstract

We proposed an ultra-fast, highly-accurate and computing resource-saving solution to optimize the maximum achievable short-circuit current and power conversion efficiency of perovskite/crystalline-silicon (c-Si) tandem solar cells via deep learning and heuristic algorithms.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2021/CP/D0CP05882C

Reference35 articles.

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3. Low-bandgap mixed tin–lead iodide perovskite absorbers with long carrier lifetimes for all-perovskite tandem solar cells