发表机构
ZJU-UIUC Institute, Zhejiang University; Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign; School of Materials Science & Engineering, Zhejiang University（浙江大学紫金港国际校区; 伊利诺伊大学厄巴纳-香槟分校机械科学与工程系; 浙江大学材料科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
通过启发式方法为LLM智能体开发拟合XRR曲线的技能，记录专家决策并形成步骤和报告模板，使智能体能自动运行X-Ray Calc 3，在测试中高精度恢复多层膜结构参数。
AI 中文摘要
周期性多层X射线反射镜的结构通过拟合其X射线反射率（XRR）曲线获得，结果取决于操作者如何归一化和裁剪曲线、释放参数以及接受拟合。拟合程序的手册和描述其引擎的论文将这些决策留给操作者，其做法是隐性的，因此拟合仍依赖于专家。为解决此问题，我们提出通过启发式方法为大型语言模型（LLM）智能体开发技能：在拟合过程中记录专家的决策，并将其写成十三个步骤和一份22项报告模板。该智能体通过模型上下文协议（MCP）工具服务器运行X-Ray Calc 3。每个新会话都赋予该技能、一条曲线和一个名义设计，并根据专家保留的拟合结果，在预先设定的六个容差下进行评分。该技能在Co/C镜和Ru/C镜的XRR曲线（来自公共数据存储库）上开发。最终版本的技能在W/B4C多层膜上测试。结果表明，该技能在两种W/B4C样品上均能在0.3 Å内恢复专家拟合的平均周期和整个堆栈的周期漂移，并在其中一个样品上在1 Å内恢复W和B4C的厚度。
英文摘要
The structure of a periodic multilayer X-ray mirror is obtained by fitting its X-ray reflectivity (XRR) curve, and the result depends on how the operator normalizes and trims the curve, frees parameters, and accepts a fit. The manual of the fitting program and the papers describing its engine leave these decisions to the operator, whose practice is tacit, so the fitting stays with the expert. To solve this problem, we proposed to develop a skill for a large language model (LLM) agent via elicitation: the expert's decisions were recorded during fitting and written as thirteen steps and a 22-item report template. The agent runs X-Ray Calc 3 through a Model Context Protocol (MCP) tool server. Fresh sessions, each given the skill, one curve, and a nominal design, were scored against fits the expert had withheld, under six tolerances fixed beforehand. The skill was developed on XRR curves of Co/C mirrors and of Ru/C mirrors from a public data deposit. The final version of the skill was tested on W/B4C multilayers. It was demonstrated that the skill recovered the mean period within 0.3 Å of the expert's fits and the period drift through the stack on both W/B4C specimens, and the W and B4C thicknesses within 1 Å on one of them.
