用大模型自动完成无定形聚合物的全原子分子模拟，从名称直接预测性能。
PolyJarvis: An LLM-Orchestrated Agent for Automated All-Atom Molecular Dynamics of Amorphous Homopolymers
- 大模型协调工具链，输入名称或SMILES即可全自动构建、模拟和计算
- 9种聚合物25项属性中18项达标，玻璃化转变误差<50K，密度误差<5%
- 适合材料模拟新手或想快速验证聚合物性能的研究者
全原子分子动力学（MD）模拟可从分子结构预测聚合物性能，但需掌握力场选择、系统构建、平衡和性质提取等专业知识。我们提出PolyJarvis，一个将大语言模型（LLM）与增强蒙特卡洛（EMC）系统构建和LAMMPS MD工具结合的智能体，通过模型上下文协议（MCP）服务器实现端到端聚合物性能预测。给定聚合物名称或SMILES，PolyJarvis自动完成建模、平衡及热/机械性质计算。在9种无定形均聚物上验证：聚乙烯（PE）、聚苯乙烯（PS）、聚甲基丙烯酸甲酯（PMMA）、聚乙二醇（PEG）、聚醚醚酮（PEEK）、聚氯乙烯（PVC）、聚乳酸（PLA）、聚砜（PSU）和顺式-聚丁二烯（cis-PBD），涵盖7种化学类型。四次重复实验平均下，25项对比中有18项符合接受标准（玻璃化转变差<50K，密度差<5%，体积模量差<30%）：玻璃化转变7/9，密度5/9，体积模量6/7。失败案例分为两类：运行密度偏低的聚合物一致力场（PCFF）系统，以及刚性骨架的PLA和PEEK在降温时高估玻璃化转变。问题均定位至工作流中的协议或分析步骤。本研究证明了大模型驱动的智能体可执行端到端聚合物MD流程，预测精度因性质和聚合物而异。
原文摘要 · Abstract (English)
All-atom molecular dynamics (MD) simulations can predict polymer properties from molecular structure, yet their execution requires specialized expertise in force field selection, system construction, equilibration, and property extraction. We present PolyJarvis, an agent that couples a large language model (LLM) with established simulation toolkits, including Enhanced Monte Carlo (EMC) for system construction and LAMMPS for molecular dynamics, through Model Context Protocol (MCP) servers, enabling end-to-end polymer property prediction from natural language input. Given a polymer name or SMILES string, PolyJarvis orchestrates molecular model construction, equilibration, and thermal/mechanical property calculation. Validation is conducted on nine amorphous homopolymers spanning seven chemistries: polyethylene (PE), polystyrene (PS), poly(methyl methacrylate) (PMMA), poly(ethylene glycol) (PEG), poly(ether ether ketone) (PEEK), poly(vinyl chloride) (PVC), poly(lactic acid) (PLA), polysulfone (PSU), and cis-polybutadiene (cis-PBD). On the replicate mean over four runs, 18 of the 25 property comparisons with experimental references meet the acceptance criteria (glass transition within 50K, density within 5%, bulk modulus within 30%): glass transition 7 of 9, density 5 of 9, and bulk modulus 6 of 7. The failures fall into two groups: polymer consistent force field (PCFF) systems that run under-dense, and the rigid backbones PLA and PEEK, which overestimate the glass transition on cooling. Each was traced to a protocol or an analysis step of the workflow. As a proof of concept, this work shows that an LLM-driven agent can carry out end-to-end polymer MD workflows, with predictive accuracy that varies across properties and polymers.
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