多智能体AI自动分析热电材料实验数据并构建知识库
Autonomous Analysis of Thermoelectric Materials Big Data by Multi-Agentic AI and Construction of a Knowledge Base and Specialized AI
一帮AI智能体分工分析热电材料数据，攒出带引用的知识库，还能查到每条结论的原始出处，做科研数据挖掘的可以看看。
研究团队开发了名为 Multi-Agentic AI（MAA）的多智能体系统，由分工不同的 AI 智能体按预设指令和质量检查流程分析热电材料实验数据，并保存可溯源的分析记录。在最大规模的实验中，系统完成了 100 次分析、覆盖 10 个主题和 10 个循环，概念共现集中在主题内部，部分关系描述在较晚的循环才被首次检出。基于该实验知识库构建的专用 AI 检索分析记录后，可回答材料学问题并附引用来源。回溯检查也发现错误：可溯源让错误可被发现，但无法杜绝错误。
Autonomous Analysis of Thermoelectric Materials Big Data by Multi-Agentic AI and Construction of a Knowledge Base and Specialized AI
Extracting knowledge from large databases of experimental materials data requires repeated analyses and reusable records of their choices and results. We developed Multi-Agentic AI (MAA), a multi-agent system in which AI agents with distinct roles analyze experimental thermoelectric data under predefined instructions and quality checks, saving analysis records traceable to their sources. In four experiments varying theme and cycle counts, we measured what accumulated in these records. In the largest experiment (100 analyses across 10 themes and 10 cycles), concept co-occurrences were concentrated within themes, vocabulary was shared across themes and some relation descriptions were first detected in later cycles. A specialized AI that searches this experiment's knowledge bases and reads their analysis records answered materials questions with cited sources. Retrospective checks, including tracing an answer generated with a preliminary corpus back to the source data, exposed errors: traceability makes errors findable but not absent.