A collaborative agent with two lightweight synergistic models for autonomous crystal materials research
Tongyu Shi, Yutang Li, Zhanyuan Li, Qian Liu, Jie Zhou, Wenhe Xu, Yang Li, Dawei Dai, Rui He, Wenhua Zhou, Jiahong Wang, Xue-Feng Yu

TL;DR
MatBrain is a lightweight, dual-model system that enhances crystal materials research by combining analytical reasoning and tool coordination, outperforming larger models and accelerating discovery processes.
Contribution
Introduction of MatBrain, a dual-model lightweight agent architecture that improves domain-specific reasoning and tool coordination in materials science research.
Findings
MatBrain outperforms larger general-purpose models in materials tasks.
It reduces hardware deployment barriers by over 95%.
Generated 30,000 candidate structures and identified 38 promising materials in 48 hours.
Abstract
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…
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