Organic solar cells (OSCs) hold great promise for sustainable energy, but discovering high-performance materials is time-consuming and costly. Existing molecular generation methods can aid the design of OSC molecules, but they are mostly confined to optimizing known backbones and lack effective use of domain-specific chemical knowledge, often leading to unrealistic candidates. In this paper, we introduce OSCAgent, a multi-agent framework for OSC molecular discovery that unifies retrieval-augmented design, molecular generation, and systematic evaluation into a continuously improving pipeline, without requiring additional human intervention. OSCAgent comprises three collaborative agents. The Planner retrieves knowledge from literature-curated molecules and prior candidates to guide design directions. The Generator proposes new OSC acceptors aligned with these plans. The Experimenter performs comprehensive evaluation of candidate molecules and provides feedback for refinement. Experiments show that OSCAgent produces chemically valid, synthetically accessible OSC molecules and achieves superior predicted performance compared to both traditional and large language model (LLM)-only baselines. Representative results demonstrate that some candidates achieve predicted efficiencies approaching 18\%. The code will be publicly available.

翻译：有机太阳能电池（OSCs）在可持续能源领域前景广阔，但高性能材料的发现过程耗时且成本高昂。现有的分子生成方法虽能辅助OSC分子设计，但大多局限于优化已知骨架结构，且未能有效利用领域特定的化学知识，常导致生成不现实的候选分子。本文提出OSCAgent，一种用于OSC分子发现的多智能体框架，它将检索增强设计、分子生成与系统评估统一整合为可自主持续优化的流程，无需额外人工干预。OSCAgent包含三个协同工作的智能体：规划器从文献整理分子及历史候选结构中检索知识以指导设计方向；生成器根据规划方案提出新型OSC受体分子；实验器对候选分子进行全面评估并提供优化反馈。实验表明，OSCAgent能够生成化学结构合理、合成可行性高的OSC分子，其预测性能均优于传统方法和仅使用大语言模型（LLM）的基线。典型结果显示，部分候选分子的预测效率接近18\%。相关代码将公开提供。