用自然语言自动完成金属有机框架模拟全流程
SimMOF: AI agent for Automated MOF Simulations
- 基于大模型的多智能体系统，将自然语言转为可执行模拟计划
- 支持从结构准备到结果分析的端到端自动化，无需专家干预
- 适合材料设计、计算化学研究者快速开展高通量模拟
金属-有机框架（MOFs）具有广阔的结构设计空间，计算模拟在预测其结构与物化性质方面至关重要。然而，由于工作流构建、参数选择、工具兼容性及结构预处理等环节需专家决策，MOF模拟仍难普及。本文提出SimMOF，一个基于大语言模型的多智能体框架，能够根据自然语言查询自动完成从头到尾的MOF模拟流程。SimMOF将用户请求转化为依赖感知的执行计划，生成可运行输入，协调多个智能体执行模拟，并以符合用户需求的方式总结分析结果。通过典型案例验证，SimMOF实现了适应性强、认知自主的工作流，体现人类研究人员的迭代决策行为，为数据驱动的MOF研究提供了可扩展基础。
原文摘要 · Abstract (English)
Metal-organic frameworks (MOFs) offer a vast design space, and as such, computational simulations play a critical role in predicting their structural and physicochemical properties. However, MOF simulations remain difficult to access because reliable analysis require expert decisions for workflow construction, parameter selection, tool interoperability, and the preparation of computational ready structures. Here, we introduce SimMOF, a large language model based multi agent framework that automates end-to-end MOF simulation workflows from natural language queries. SimMOF translates user requests into dependency aware plans, generates runnable inputs, orchestrates multiple agents to execute simulations, and summarizes results with analysis aligned to the user query. Through representative case studies, we show that SimMOF enables adaptive and cognitively autonomous workflows that reflect the iterative and decision driven behavior of human researchers and as such provides a scalable foundation for data driven MOF research.
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