用AI自动完成聚合物粗粒化模拟，省时省力还精准。
A Multi-Agent Framework for Automated Coarse-Grained Molecular Dynamics of Polymers
- 用大模型理解聚合物名称，自动生成粗粒化拓扑结构。
- 27个任务全部完成，密度误差小于5%，模拟速度提升至1分钟。
- 适合需要快速构建聚合物模型的研究者，尤其擅长复杂结构。
粗粒化分子动力学可突破全原子方法的尺度限制，但自下而上的粗粒化建模耗时费力。由于粗粒化分辨率是设计选择，通用参数集通常不可用，需为每种聚合物映射重新推导势函数。本文提出CGMas多智能体框架，仅需自然语言描述聚合物及目标分辨率，即可自动化完成拓扑构建、系统平衡、映射、势函数推导与验证。大语言模型推理智能体从聚合物名称推断全原子拓扑，分层自我修正机制解决不饱和、杂原子和极性聚合物中的常见物理错误。下游智能体完成系统平衡、粗粒化映射、通过玻尔兹曼反演推导势函数，并与原子参考模型对比验证。在27个均聚物与共聚物任务中，CGMas全部完成，22个任务的密度与原子模型偏差小于5%，模拟时间从38–88分钟缩短至1分钟，证明代理式大模型是实现聚合物粗粒化自动化的有效路径。
原文摘要 · Abstract (English)
Coarse-grained (CG) molecular dynamics extends polymer simulation beyond the scales accessible to all-atom (AA) methods, but bottom-up CG modeling is laborious. The CG resolution is a design choice, so a transferable parameter set is generally not available and the potentials are derived anew for each polymer mapping. Here we present CGMas, a multi-agent framework that automates topology construction, equilibration, mapping, potential derivation, and validation from a natural-language specification of the polymer and target resolution. A large-language-model (LLM) reasoning agent infers the AA topology from polymer name, while layered self-correction resolves physical errors common to unsaturated, heteroatom-containing, and polar polymers. Downstream agents equilibrate the system, map it onto CG representation, derive potentials through Boltzmann inversion, and benchmark the model against its atomistic reference. CGMas completed all 27 homopolymer and copolymer tasks, matched the AA density to within 5% in 22, and reduced simulation from 38-88 min to 1 min, establishing agentic LLMs as a route to automated polymer coarse-graining.
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