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arXiv 2501.00564cond-mat.mtrl-scics.DL
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大语言模型驱动的热电材料数据库

Large Language Model-Driven Database for Thermoelectric Materials

Suman Itani, Yibo Zhang, Jiadong Zang

AI总结:

本文构建了由大语言模型驱动的包含7,123种热电化合物的开放数据库,利用GPTArticleExtractor自动提取文献数据,以解决人工收集难题并推动热电材料的数据驱动发现。

摘要:

热电材料为将废热转化为电能提供了一种可持续的途径。然而,由于缺乏可靠的数据库,这些材料的数据驱动发现与优化面临挑战。在此,我们构建了一个包含7,123种热电化合物的综合数据库,其中包含化学成分、结构细节、塞贝克系数、电导率与热导率、功率因子以及热电优值(ZT)等关键信息。我们使用由大语言模型(LLM)驱动的GPTArticleExtractor工作流,从Elsevier期刊发表的科学文献中自动提取和整理数据。这一过程使得结构化数据库的创建成为可能,从而解决了人工数据收集的难题。该开放获取数据库有望推动数据驱动研究,并促进热电材料的分析与发现。

英文摘要:

Thermoelectric materials provide a sustainable way to convert waste heat into electricity. However, data-driven discovery and optimization of these materials are challenging because of a lack of a reliable database. Here we developed a comprehensive database of 7,123 thermoelectric compounds, containing key information such as chemical composition, structural detail, seebeck coefficient, electrical and thermal conductivity, power factor, and figure of merit (ZT). We used the GPTArticleExtractor workflow, powered by large language models (LLM), to extract and curate data automatically from the scientific literature published in Elsevier journals. This process enabled the creation of a structured database that addresses the challenges of manual data collection. The open access database could stimulate data-driven research and advance thermoelectric material analysis and discovery.

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。

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