AI 中文总结
研究旨在加速金属有机框架用于气体分离的逆设计。提出LEMO智能体框架，结合多种技术，经迭代循环指导搜索。在分离任务中评估，相比基线有优势，丰富候选、提性能、保多样，还经实验筛选实现合成与表征，证明大语言模型智能体可加速MOF发现。
AI 中文摘要
金属有机框架（MOFs）为吸附性气体分离提供了高度模块化的平台，但其庞大的网状设计空间使得在化学有效性、分离性能和结构多样性的同时约束下进行逆设计变得困难。在此，我们提出了LEMO智能体，这是一个用于在MOFid空间中进行气体分离MOFs闭环逆设计的大语言模型智能体框架。LEMO智能体将基于语言的候选生成与MOFid标准化、显式有效性检查、基于Transformer的性质预测、结构化设计记忆和多岛探索相结合。通过迭代的生成-验证-评估-记忆循环，智能体利用成功和失败候选者的反馈来指导在连接体、金属和拓扑选择上的化学约束搜索。我们在CH₄/N₂和CO₂/N₂分离任务上评估了LEMO智能体。与代表性的生成、优化和智能体基线相比，LEMO智能体丰富了高性能候选者，提高了预测的分离性能，并保持了广泛的化学和拓扑多样性。选定的候选者进一步进行重构，通过GCMC模拟进行评估，并通过基于化学可行性和配体可购买性的实验筛选工作流程，从而实现初步的湿实验室合成和SEM表征。这些结果表明，大语言模型智能体可以作为可解释和可扩展的设计引擎，加速MOF的发现，超越传统的固定库筛选。
英文摘要
Metal-organic frameworks (MOFs) offer a highly modular platform for adsorptive gas separation, yet their vast reticular design space makes inverse design difficult under simultaneous constraints of chemical validity, separation performance, and structural diversity. Here, we present LEMO Agent, a large-language-model agent framework for closed-loop inverse design of gas-separation MOFs in MOFid space. LEMO Agent couples language-based candidate generation with MOFid standardization, explicit validity checking, Transformer-based property prediction, structured design memory, and multi-island exploration. Through iterative generate--validate--evaluate--remember cycles, the agent uses feedback from both successful and failed candidates to guide chemically constrained search across linker, metal, and topology choices. We evaluate LEMO Agent on CH$_4$/N$_2$ and CO$_2$/N$_2$ separation tasks. Compared with representative generative, optimization, and agentic baselines, LEMO Agent enriches high-performing candidates, improves predicted separation performance, and maintains broad chemical and topological diversity. Selected candidates are further reconstructed, evaluated by GCMC simulations, and passed through an experimental down-selection workflow based on chemical feasibility and ligand purchasability, leading to initial wet-lab synthesis and SEM characterization. These results demonstrate that large language model agents can serve as interpretable and scalable design engines for accelerating MOF discovery beyond conventional fixed-library screening.
Comments19 pages,5 figures
