Agent-MD：用于有状态GCMC-MD模拟流程的带事件驱动升级机制的选择性大语言模型干预框架
Agent-MD: Selective LLM Intervention with Event-Driven Escalation for Stateful GCMC--MD Campaigns
- The Hong Kong Polytechnic University（香港理工大学）
- The Hong Kong University of Science and Technology（香港科技大学）
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
Agent-MD框架将LLM推理选择性用于GCMC-MD分子模拟流程的构建与事件审查，结合确定性执行，在蒙脱石水蒸汽脱附模拟中完成多周期计算，识别问题并揭示体系依赖的低湿度响应，实现可复现的代理辅助分子模拟。
AI中文摘要：
长期运行的分子模拟流程需要从保存的状态反复续算、溯源感知的进度推进、自适应评估，以及对固定规则无法安全解决的工作流条件的偶尔解读。在此，我们提出Agent-MD，这一框架将大语言模型（LLM）推理选择性地应用于流程构建和事件触发的审查环节，而常规的模拟、分析、续算、归档及状态推进则由持久的基于规则的流程代理，利用已批准的策略和显式状态记录来处理。Agent-MD在一个包含5个蒙脱石体系和3个相对湿度（RH）状态（RH=0.9-0.3-0.1）的 grand canonical Monte Carlo-分子动力学（GCMC-MD）水蒸汽脱附流程中得到验证。在15个体系-RH状态下，该工作流完成了120个分段模拟周期，每个周期具有对应状态的采样长度和溯源感知的重启继承机制。常规生产环节无需调用实时推理代理，仅1个状态达到审查边界；随后通过盲法推理代理重放评估了2个留存事件，识别出潜在的工作流问题并推荐了合适的后续行动。这些模拟还揭示了不同体系依赖的低RH响应：含钙蒙脱石比含钠和钾的体系保留了更多层间水并维持更大的基底间距，而电荷最高的含钠体系在干燥条件下保留了更多残留水。这些结果表明，长期运行的科学工作流无需将所有操作置于LLM推理循环中：选择性推理可与确定性执行、结构化证据及验证过的控制交接相结合，以提供可复现、可审计的代理辅助分子模拟。
英文摘要：
Long-running molecular simulation campaigns require repeated continuation from saved states, provenance-aware progression, adaptive assessment, and occasional interpretation of workflow conditions that cannot be resolved safely by fixed rules. Here, we present Agent-MD, a framework that places large language model (LLM) reasoning selectively at campaign construction and event-triggered review, while routine simulation, analysis, continuation, archiving, and state progression are handled by a persistent rule-based campaign agent using approved policies and explicit state records. Agent-MD was demonstrated in a grand canonical Monte Carlo-molecular dynamics (GCMC-MD) water-vapor desorption campaign comprising five montmorillonite systems and three sequential relative-humidity states (RH = 0.9-0.3-0.1). Across 15 system-RH states, the workflow completed 120 segmented simulation cycles with state-specific sampling lengths and provenance-aware restart inheritance. Routine production required no live reasoning-agent invocation, while one state reached a review boundary; two preserved incidents were subsequently evaluated through blinded reasoning-agent replay, which identified the underlying workflow problems and recommended appropriate follow-up actions. The simulations also revealed distinct composition-dependent low-RH responses, with Ca-bearing montmorillonite retaining more interlayer water and maintaining a larger basal spacing than the Na- and K-bearing systems, while the highest-charge Na system retained more residual water under dry conditions. These results demonstrate that long-running scientific workflows need not place every operation inside an LLM reasoning loop: selective reasoning can instead be combined with deterministic execution, structured evidence, and validated control handoffs to provide reproducible and auditable agent-assisted molecular simulation.
