Autonomous closed-loop framework for reproducible perovskite solar cells | Nature
Subjects
- Energy
- Solar cells
Abstract
The commercialization of perovskite solar cells is bottlenecked by inefficient, trial-and-error approaches reliant on human expertise in both material discovery and device fabrication (1-3). Here, we introduce an autonomous closed-loop framework that integrates machine learning (ML)-driven material discovery with an automated manufacturing platform. The system employs active learning and quantum modeling to rapidly identify high-performance molecules, while the platform uses Bayesian optimization and symbolic regression in a feedback loop to continuously refine the fabrication process. This integrated approach enabled the discovery of a passivation molecule, 5-(aminomethyl)nicotinonitrile hydroiodide (5ANI), which yielded 0.05 cm² solar cells with a power conversion efficiency (PCE) of 27.22% (certified maximum power point tracking (MPPT) efficiency of 27.18%) and 21.4 cm² mini-modules with a PCE of 23.49%. Moreover, the devices exhibited long-term operational stability, retaining 98.7% of their initial efficiency after 1,200 hours of continuous operation under the ISOS-L-1I protocol. Crucially, the automated platform achieved an efficiency reproducibility nearly 5 times that of manual fabrication. This work establishes an automated closed-loop system that synergizes ML-powered discovery with the high-fidelity data from automated manufacturing, setting a benchmark for autonomous discovery and manufacturing in photovoltaics and materials.
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Author information
Author notes- Xianglang Sun
(孙祥浪)
Present address: Hubei Key Laboratory of Material Chemistry and Service Failure, Key Laboratory for Material Chemistry of Energy Conversion and Storage, Ministry of Education, School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan, P. R. China
- These authors contributed equally: Danpeng Gao, Shuaihua Lu, Chunlei Zhang, Ning Wang, Zexin Yu, Xianglang Sun
Authors and Affiliations
- Department of Chemistry, City University of Hong Kong, Kowloon, Hong Kong, China
Danpeng Gao
(高丹鹏), Chunlei Zhang
(张春雷), Ning Wang
(王宁), Zexin Yu
(余泽鑫), Xianglang Sun
(孙祥浪), Francesco Vanin, Liangchen Qian
(钱良辰), Bo Li
(李博) & Zonglong Zhu
(朱宗龙)
- Department of Materials Science & Engineering, City University of Hong Kong, Kowloon, Hong Kong, China
Shuaihua Lu
(陆帅华), Nan Li
(李楠) & Xiao Cheng Zeng
(曾晓成)
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
Rebecca Martin & Samuel D. Stranks
- Department of Chemistry, Imperial College London; MSRH Building, White City Campus, London, UK
Francesco Vanin, Nicholas Long & Nicola Gasparini
- Institute of Materials for Electronics and Energy Technology (i-MEET), Department of Materials Science and Engineering, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, Erlangen, Germany
Larry Lüer & Christoph Joseph Brabec
- School of Materials Science and Engineering, Central South University, Changsha, P. R. China
Bo Li
(李博)
- Electronic Engineering Department, The Chinese University of Hong Kong, New Territories, Hong Kong, China
Martin Stolterfoht
- Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, China
Junhui Hou
(侯军辉)
- Department of Applied Physics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
Jun Yin
(殷骏)
- Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, China
Yen-Hung Lin
(林彥宏)
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
Haipeng Lu
(吕海鹏)
- Helmholtz-Institute Erlangen-Nürnberg for Renewable Energy (HI ERN), Forschungszentrum Jülich, Erlangen, Germany
Christoph Joseph Brabec
- Energy Campus Nürnberg (EnCN), Fürtherstrasse 250, Nürnberg, Germany
Christoph Joseph Brabec
- Institute of Materials Data Science and Informatics (IMD-3), Forschungszentrum Jülich, Jülich, Germany
Christoph Joseph Brabec
- Hong Kong Institute for Clean Energy, City University of Hong Kong, Kowloon, Hong Kong, China
Zonglong Zhu
(朱宗龙)
Authors- Danpeng Gao
(高丹鹏)View author publications
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- Shuaihua Lu
(陆帅华)View author publications
Search author on:PubMed Google Scholar
- Chunlei Zhang
(张春雷)V