# Large Language Model-Driven Database for Thermoelectric Materials

arXiv: https://arxiv.org/abs/2501.00564 · HTML: https://mlsanity.com/paper/2501.00564/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2024.

## Built on by (most cited first, 3 of 3)

- [Automated Extraction of Material Properties using LLM-based AI Agents](https://mlsanity.com/paper/2510.01235/) (2025)
- [Lattice-to-Total Thermal Conductivity Ratio: A Phonon-Glass Electron-Crystal Descriptor for Data-Driven Thermoelectric Design](https://mlsanity.com/paper/2511.21213/) (2025)
- [Large language model-enabled automated data extraction for concrete materials informatics](https://mlsanity.com/paper/2604.22938/) (2026)

## Abstract (arXiv)

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.