Came across this dataset of thermolectric data while searching for some permanent magnet data. They use LLMs to parse papers and extract a structured database.
https://arxiv.org/abs/2501.00564
From the abstract:
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.
Looking forward to it.
My interest is in discovering low cost, highly scalable thermoelectric materials made from naturally abundant elements. Not necessarily high power, low power would be fine. For application in remote and impoverished regions. I don't expect this material to exist tbh, but if there's a slight chance it does then I'd like to find it!
awesome thanks. have downloaded and am about to explore it.
I also recently found a set here (although also mined from Elsevier + Springer so may be some overlap):
https://www.royce.ac.uk/programmes/digital-materials-foundry/experimental-materials-data-library/
This comes from the Digital Materials Foundry:
https://www.royce.ac.uk/programmes/digital-materials-foundry/
Digital Materials Foundry might be one to keep an eye on, there's also a semiconductor materials dataset you might be interested in as well as lots more related to materials informatics type stuff.
Nice! I haven't seen this source before. Looks like a similar approach - extraction from papers.
I came across some first-principles calculations relevant to thermoelectrics that may be useful. I'll write up a post on it in the next couple days. It uses GNNs so anyone can run them without needed access to HPC for DFT ab initio calculations.
Is your interest in discovering more effective thermoelectric materials or something else