AI系统自动生成可合成的金属有机框架材料，加速碳捕集与水收集应用。
System of Agentic AI for the Discovery of Metal-Organic Frameworks
- 多智能体协作：语言模型提结构、扩散模型生成晶体、量子与合成性代理优化筛选。
- 生成超30万种新MOF结构和可合成配体，5个AI设计材料成功实验合成。
- 适合材料发现、自动化合成及绿色能源领域研究者参考。
生成模型与机器学习有望加速金属有机框架（MOFs）在二氧化碳捕集和水收集中的发现，但面临巨大化学空间搜索与可合成性保障的挑战。本文提出MOFGen系统，由多个智能体组成：大语言模型提出新型MOF组成，扩散模型生成晶体结构，量子力学代理优化并筛选候选物，合成可行性代理基于专家规则与机器学习进行判断。该系统基于所有已实验报道的MOFs及计算数据库训练，生成了数十万种新的MOF结构和可合成有机链接体。通过高通量实验验证，成功合成了5个‘AI构想’的MOF材料，标志着向自动化可合成材料发现迈出重要一步。
原文摘要 · Abstract (English)
Generative models and machine learning promise accelerated material discovery in MOFs for CO2 capture and water harvesting but face significant challenges navigating vast chemical spaces while ensuring synthetizability. Here, we present MOFGen, a system of Agentic AI comprising interconnected agents: a large language model that proposes novel MOF compositions, a diffusion model that generates crystal structures, quantum mechanical agents that optimize and filter candidates, and synthetic-feasibility agents guided by expert rules and machine learning. Trained on all experimentally reported MOFs and computational databases, MOFGen generated hundreds of thousands of novel MOF structures and synthesizable organic linkers. Our methodology was validated through high-throughput experiments and the successful synthesis of five "AI-dreamt" MOFs, representing a major step toward automated synthesizable material discovery.
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