基于技能的自主代理用于材料蠕变数据库构建
Skill-Based Autonomous Agents for Material Creep Database Construction
AI总结：
本文提出基于技能的自主代理框架，用于从科学文献中自动提取高保真的材料蠕变数据，实现物理自洽的数据库构建。
摘要：
数据驱动材料科学的进步目前受到一个根本性瓶颈的限制：大多数历史实验数据仍然被锁定在传统科学文献中的非结构化文本和位图图像中。手动整理这种知识是极其耗费人力且容易出错的。为了解决这一挑战，我们引入了一个由大型语言模型（LLMs）驱动的自主代理框架，旨在从科学PDF中无人工干预地挖掘高保真的数据集。通过部署一个模块化的
英文摘要：
The advancement of data-driven materials science is currently constrained by a fundamental bottleneck: the vast majority of historical experimental data remains locked within the unstructured text and rasterized figures of legacy scientific literature. Manual curation of this knowledge is prohibitively labor-intensive and prone to human error. To address this challenge, we introduce an autonomous, agent-based framework powered by Large Language Models (LLMs) designed to excavate high-fidelity datasets from scientific PDFs without human intervention. By deploying a modular "skill-based" architecture, the agent orchestrates complex cognitive tasks - including semantic filtering, multi-modal information extraction, and physics-informed validation. We demonstrate the efficacy of this framework by constructing a physically self-consistent database for material creep mechanics, a domain characterized by complex graphical trajectories and heterogeneous constitutive models. Applying the pipeline to 243 publications, the agent achieved a verified extraction success rate exceeding 90% for graphical data digitization. Crucially, we introduce a cross-modal verification protocol, demonstrating that the agent can autonomously align visually extracted data points with textually extracted constitutive parameters ($R^2 > 0.99$), ensuring the physical self-consistency of the database. This work not only provides a critical resource for investigating time-dependent deformation across diverse material systems but also establishes a scalable paradigm for autonomous knowledge acquisition, paving the way for the next generation of self-driving laboratories.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
