Language-model-guided robotic boxes advance perovskite solar cell discovery

Perovskite solar cells have emerged as one of the most promising next-generation photovoltaic technologies, but their development still depends heavily on time-consuming trial-and-error synthesis and labor-intensive device fabrication. Researchers have already explored more than one hundred thousand recipes to improve device performance, yet the formulas remain complex, additives are highly diverse, and crystallization is extremely sensitive to environmental conditions. As a result, fabrication remains difficult to control, while the related physical and chemical mechanisms are still not fully understood. Although high-throughput robotic systems can accelerate data collection, they often struggle to analyze rapidly growing numerical datasets effectively or to provide timely feedback for semantic recipe optimization and mechanistic reasoning at the device scale.

Researchers from the Hong Kong Polytechnic University and collaborating institutions report an agentic robotics system for perovskite solar cell research in Engineering in 2026. The work combines a language agent, a domain-specific recipe language model (RLM), and 11 interconnected robotic boxes within a unified framework for synthesis, fabrication, characterization, and feedback-driven optimization. Using this system, the team carried out 50,764 perovskite solar cell device experiments, achieved a champion power conversion efficiency of 27.0%, with a certified value of 26.5%, and generated more than 578 million tokens to strengthen recipe recommendation and mechanistic reasoning.

At the core of the study is the idea that robotic experimentation should do more than automate repeated operations. The researchers designed a seven-layer artificial intelligence (AI) architecture covering learning, generating, RecipeQA, fine-tuning, reasoning, evaluation, and optimization. Within this framework, both numerical and semantic recipes can be continuously learned from literature corpora and robot-generated corpora, enabling iterative refinement of the RLM. Formulas and parameters are encoded into machine-readable recipes, translated into robot-executable commands, and returned as structured feedback after fabrication and characterization. In this way, the system establishes a closed-loop workflow linking recommendation, execution, validation, and model improvement.

The hardware system upgrades an earlier robotic synthesis system into a full-device fabrication system for perovskite solar cells. A digital twin serves as a real-time software–hardware interface, translating model-generated recipes into executable robotic instructions while synchronizing experimental states and feedback. The 11 robotic boxes form an enclosed and interconnected environment for synthesis, fabrication, and characterization. Altogether, the system includes 101 functional modules, more than 1,500 components, and 4,300 controllable parameters, reconstructing traditionally fragmented glovebox-based manual operations into coupled robotic execution.

According to the researchers, the key advance is the integration of three advantages within one closed-loop AI–robotics framework: controllable fabrication of full perovskite solar cell devices by robotic boxes, robotic characterization that converts high-throughput experimental outputs into structured mechanism-related evidence, and domain-specific RLM which is trained and continuously improves recipe recommendation, mechanistic reasoning, and subsequent robotic execution.

The significance of the work extends beyond perovskite photovoltaics. By integrating a language agent, an RLM, robotic fabrication, robotic characterization, and feedback-driven optimization into one research framework, the study provides a practical route toward next-generation materials research tools. More broadly, this work highlights a paradigm shift from manual discovery, providing a scalable architectural foundation of materials intelligence. In the longer term, such AI and robotics systems could be deployed in extreme environments to support on-site materials intelligent manufacturing.

The article, titled “Agentic Robotic Boxes for Perovskite Solar Cell Fabrication with Recipe Language Model,” was authored by 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. It was published in the journal Engineering. Full text of the open access paper: https://doi.org/10.1016/j.eng.2026.04.002. For more information about Engineering, visit the website at https://www.sciencedirect.com/journal/engineering.