The chemical reaction recommendation is to select proper reaction condition parameters for chemical reactions, which is pivotal to accelerating chemical science. With the rapid development of large language models (LLMs), there is growing interest in leveraging their reasoning and planning capabilities for reaction condition recommendation. Despite their success, existing methods rarely explain the rationale behind the recommended reaction conditions, limiting their utility in high-stakes scientific workflows. In this work, we propose ChemMAS, a multi-agent system that reframes condition prediction as an evidence-based reasoning task. ChemMAS decomposes the task into mechanistic grounding, multi-channel recall, constraint-aware agentic debate, and rationale aggregation. Each decision is backed by interpretable justifications grounded in chemical knowledge and retrieved precedents. Experiments show that ChemMAS achieves 20-35% gains over domain-specific baselines and outperforms general-purpose LLMs by 10-15% in Top-1 accuracy, while offering falsifiable, human-trustable rationales, which establishes a new paradigm for explainable AI in scientific discovery.

翻译：化学反应推荐旨在为化学反应选择合适的反应条件参数，这对加速化学科学至关重要。随着大型语言模型（LLMs）的快速发展，利用其推理与规划能力进行反应条件推荐的研究日益受到关注。尽管现有方法取得了一定成功，但它们很少解释推荐反应条件背后的依据，从而限制了其在高风险科学工作流程中的实用性。本文提出了ChemMAS——一个将条件预测重构为基于证据的推理任务的多智能体系统。ChemMAS将该任务分解为机理基础、多渠道检索、约束感知的智能体辩论以及依据聚合。每个决策均由基于化学知识和检索到的先例的可解释理由支撑。实验表明，ChemMAS在域特定基线上实现了20-35%的性能提升，并在Top-1准确率上比通用LLMs高出10-15%，同时提供可证伪、值得人类信赖的解释，从而为科学发现中的可解释人工智能建立了新范式。