用于聚合物自动化粗粒度分子动力学的多智能体框架
A Multi-Agent Framework for Automated Coarse-Grained Molecular Dynamics of Polymers
- Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出CGMas多智能体框架，可自动完成聚合物粗粒度分子动力学的拓扑构建等全流程，完成27项任务，22项密度匹配度在5%内，模拟时间大幅缩短，确立了智能体式LLM用于聚合物粗粒化的可行性。
AI中文摘要：
粗粒度（CG）分子动力学将聚合物模拟扩展到了全原子（AA）方法无法企及的尺度，但自底向上的CG建模十分繁琐。CG分辨率是一项设计选择，因此通常无法获得可迁移的参数集，且需为每种聚合物映射重新推导势能。本文提出CGMas，一个多智能体框架，可根据聚合物及目标分辨率的自然语言说明，自动完成拓扑构建、平衡化、映射、势能推导及验证。大型语言模型（LLM）推理智能体根据聚合物名称推断AA拓扑，分层自校正可解决不饱和、含杂原子及极性聚合物常见的物理误差。下游智能体使系统平衡化、将其映射至CG表示、通过玻尔兹曼反演推导势能，并以原子级参考对模型进行基准测试。CGMas完成了全部27项均聚物与共聚物任务，其中22项的AA密度匹配度在5%以内，模拟时间从38-88分钟缩短至1分钟，确立了智能体式LLM作为自动化聚合物粗粒化的一条可行路径。
英文摘要：
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.
