用自进化多智能体系统自动发现新型催化剂，精准预测反应性能。
Autonomous heterogeneous catalyst discovery with a self-evolving multi-agent digital twin
- 八类智能体协同建模气固/液固界面，从晶体和语言描述出发预测催化路径。
- 5-30分钟内完成全流程计算，关键反应能垒预测误差小于2倍实验值。
- 适用于多种复杂催化体系，可自主发现媲美贵金属的非贵金属候选材料。
理论催化研究有望加速催化剂发现，但现有计算与机器学习预测常偏离实验结果，且局限于少数材料体系，缺乏忠实、条件感知的催化模拟器。本文提出CatDT（催化数字孪生），一种自进化多智能体系统，构建工作催化剂的自动化数字孪生体，统一处理气固与液固过程。仅需块体晶体结构和自然语言反应描述，八个专业智能体与27个科学工具可在单张GPU上5至30分钟内完成稳定晶面预测、工作表面重构、反应路径枚举与排序、过渡态定位及动力学计算。两项创新解决核心难题：UniMech通过智能体引导与能量缓存图搜索融合，使新物料主导路径发现成本降低超过1000倍；记忆增强强化学习环将能垒计算成功率从41%提升至84%，覆盖600个催化表面。在七个气固基准测试中——包括台阶金属、单原子催化剂、有序金属间化合物、富缺陷二维硫化物/碳化物及强金属-载体相互作用（SMSI）界面——所有预测结果与实验值偏差在0.5至2倍之间，跨越四个数量级。对于丙烷脱氢反应，CatDT独立发现非贵金属候选材料，性能媲美铂基工业催化剂，提出的Ni@ZrO₂ SMSI包覆层模拟周转频率达1.63 s⁻¹，选择性接近100%。更广泛而言，真实催化数字孪生或任何多阶段科学模拟器的关键，并非大模型原始能力，而是工程化设计：确定性工具、持续记忆与经验证的自我优化机制，可在模型、工具与运行间累积增益。
原文摘要 · Abstract (English)
Theoretical heterogeneous catalysis promises rapid catalyst discovery, yet computational and machine-learning predictions often deviate from experiment and stay confined to narrow material families, for want of a faithful, condition-aware catalytic simulator. We present CatDT (Catalysis Digital Twin), a self-evolving multi-agent system that builds an autonomous digital twin of a working catalyst, unifying gas-solid and liquid-solid modeling. From only a bulk crystal and a natural-language reaction description, eight specialized agents and 27 scientific tools predict stable facets, reconstruct working surfaces, enumerate and rank reaction pathways, locate transition states, and compute kinetics in 5-30 min on a single GPU. Two innovations address the hardest steps: UniMech finds dominant pathways for novel materials at over $10^3\times$ lower cost than exhaustive enumeration by fusing agent-guided proposals with energy-cached graph search, and a memory-augmented reinforcement loop raises barrier-calculation success from 41% to 84% across 600 catalytic surfaces. Across seven gas-solid benchmarks -- stepped metals, single-atom catalysts, ordered intermetallics, vacancy-rich 2D sulfides and carbides, and a strong-metal--support-interaction (SMSI) interface -- every CatDT prediction lies within 0.5-2 times experiment over four orders of magnitude. For propane dehydrogenation, CatDT independently discovers non-precious candidates rivaling the Pt-based industrial benchmark, with a proposed Ni@ZrO$_2$ SMSI overlayer reaching a simulated TOF of $1.63~\text{s}^{-1}$ at $\sim$100% selectivity. More broadly, the decisive factor for a faithful catalyst digital twin -- or any multi-stage scientific simulator -- is not raw LLM capability but the engineered harness around it: deterministic tools, persistent memory, and verified self-improvement that compound across models, tools, and runs.
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