Current large language models require hundreds of billions of parameters yet struggle with domain-specific reasoning and tool coordination in materials science. Here, we present MatBrain, a lightweight collaborative agent system with two synergistic models specialization for crystal materials research. MatBrain employs a dual-model architecture: Mat-R1 (30B parameters) as the analytical model providing expert-level domain reasoning, and Mat-T1 (14B parameters) as the executive model orchestrating tool-based actions. Entropy analysis confirms that this architecture resolves the conflict between tool planning and analytical reasoning by decoupling their distinct entropy dynamics. Enabled by this dual-model architecture and structural efficiency, MatBrain significantly outperforms larger general-purpose models while reducing the hardware deployment barrier by over 95%. MatBrain exhibits versatility across structure generation, property prediction, and synthesis planning tasks. Applied to catalyst design, MatBrain generated 30,000 candidate structures and identified 38 promising materials within 48 hours, achieving approximately 100-fold acceleration over traditional approaches. These results demonstrate the potential of lightweight collaborative intelligence for advancing materials research capabilities.

翻译：当前大型语言模型需数千亿参数，但在材料科学领域特定推理与工具协调方面仍存在不足。本文提出MatBrain——一个面向晶体材料研究的轻量级协作智能体系统，采用双模型架构实现协同专业化：Mat-R1（300亿参数）作为分析模型提供专家级领域推理能力，Mat-T1（140亿参数）作为执行模型统筹基于工具的操作。熵分析证实，该架构通过解耦工具规划与分析推理各自独立熵动态特性，解决了二者之间的冲突。凭借双模型架构与结构性效率，MatBrain显著超越更大规模的通用模型，同时将硬件部署门槛降低95%以上。该模型在结构生成、性质预测及合成规划任务中展现出通用性。应用于催化剂设计时，MatBrain在48小时内生成30,000个候选结构并识别出38种有前景的材料，相较传统方法实现约100倍加速。这些结果彰显了轻量协作智能在提升材料研究能力方面的潜力。