Agentic Robotic Boxes for Perovskite Solar Cell Fabrication with Recipe Language Model

Zijian Chen; Wenjin Yu; Chuang Wu; Feibei Chen; Zixuan Wang; Chao Zhou; Yimeng You; Shaojie Li; Qiyuan Zhu; Ning Ma; Yao Sun; Donghui Li; Billy Fanady; Shengchou Jiang; Zhongliang Yan; Shumin Zhou; Liang Li; Chang-Yu Hsieh; Yang Bai; Lixin Xiao; Chi-yung Chung; Ching-chuen Chan; Zhanfeng Cui; Michael Grätzel; Haitao Zhao

doi:10.1016/j.eng.2026.04.002

Engineering ›› :202604002 DOI: 10.1016/j.eng.2026.04.002

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Agentic Robotic Boxes for Perovskite Solar Cell Fabrication with Recipe Language Model

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^aResearch Centre for Materials Intelligent Manufacturing, State Key Laboratory of Ultra—Precision Machining Technology, Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University (PolyU) , Hong Kong 999077, China

^b Laboratory of Photonics and Interfaces, Ecole Polytechnique Fédérale de Lausanne , Lausanne 1015, Switzerland

^c Physical AI Evolution (PAlEvo) Laboratory , AI for Energy Key Laboratory, PolyU—WIT Research Centre for Materials Intelligent Manufacturing, Wenzhou Institute of Technology (WIT), Wenzhou 325000, China

^d Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China , Ningbo 315100, China

^e Faculty of Materials Science and Energy Engineering, Shenzhen University of Advanced Technology , Shenzhen 518107, China

^f State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of New Energy, North China Electric Power University , Beijing 102206, China

^g College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, China

^h State Key Laboratory of Artificial Microstructure and Mesoscopic Physics, School of Physics , Peking University, Beijing 100871, China

ⁱ Department of Engineering Science, University of Oxford , Oxford OX1 3PJ, UK

^*E-mail addresses: michael.graetzel@epﬂ.ch (M. Grätzel),

hai-tao.zhao@polyu.edu.hk (H. Zhao).

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Abstract

Perovskite solar cells (PSCs) have been undergoing rapid development with the vast combinatorial explo- ration of recipes; however, the related research suffers from time-consuming trial-and-error synthesis and labor-intensive fabrication. As a promising alternative, interconnected robotic boxes that integrate fabrication and characterization enable high-throughput experimentation and data collection; however, the resulting numerical datasets are often insufﬁciently analyzed and fail to provide effective feedback for semantic recipe optimization. Here, we conceived and realized an emerging scientiﬁc tool of robotic boxes enabled by a domain-speciﬁc recipe language model (RLM) and a coordinating language agent for PSCs research. The developed agent features two loops of seven artiﬁcial intelligence (AI) layers, in which both numerical and semantic recipes were continuously learned and optimized from the literature and robotic corpora for iterative ﬁne-tuning of the RLM. Guided by the agent, 11 robotic boxes executed the controllable synthesis, fabrication, and characterization of 50 764 PSCs, increasing the power conver- sion efﬁciency (PCE) to 27.0% (26.5% certiﬁed). Simultaneously, more than 578 million tokens were gen- erated and augmented to improve the ability to recommend a recipe and mechanistic reasoning, achieving an overall score of about 80% based on the dedicated evaluation criteria. Thus, such agentic robotic boxes provide an advanced tool for the next-generation synthesis, fabrication, characterization, and even mechanistic reasoning of PSCs and beyond.

Keywords

Robotics / Language agent / Recipe language model / Materials intelligence / Perovskite solar cells / Device fabrication

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Zijian Chen, Wenjin Yu, Chuang Wu, Feibei Chen, Zixuan Wang, Chao Zhou, Yimeng You, Shaojie Li, Qiyuan Zhu, Ning Ma, Yao Sun, Donghui Li, Billy Fanady, Shengchou Jiang, Zhongliang Yan, Shumin Zhou, Liang Li, Chang-Yu Hsieh, Yang Bai, Lixin Xiao, Chi-yung Chung, Ching-chuen Chan, Zhanfeng Cui, Michael Grätzel, Haitao Zhao. Agentic Robotic Boxes for Perovskite Solar Cell Fabrication with Recipe Language Model. Engineering 202604002 DOI:10.1016/j.eng.2026.04.002

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