A High-Quality Thermoelectric Material Database with Self-Consistent ZT Filtering

TL;DR

This paper introduces a curated thermoelectric material database, teMatDb, with a self-consistent ZT filtering method to ensure data quality, enabling advanced research and machine learning applications in thermoelectric materials.

Contribution

The study develops a high-quality thermoelectric database with a novel self-consistent ZT filtering protocol to identify and exclude inconsistent data, improving data reliability for research.

Findings

Created teMatDb with 14,717 data points from 272 TEP sets.

Developed a self-consistent ZT filtering method to detect and exclude errors.

Provided a robust dataset for data-driven thermoelectric research.

Abstract

This study presents a curated thermoelectric material database, teMatDb, constructed by digitizing literature-reported data. It includes temperature-dependent thermoelectric properties (TEPs), Seebeck coefficient, electrical resistivity, thermal conductivity, and figure of merit (ZT), along with metadata on materials and their corresponding publications. A self-consistent ZT (Sc-ZT) filter set was developed to measure ZT errors by comparing reported ZT's from figures with ZT's recalculated from digitized TEPs. Using this Sc-ZT protocol, we generated tMatDb272, comprising 14,717 temperature-property pairs from 272 high-quality TEP sets across 262 publications. The method identifies various types of ZT errors, such as resolution error, publication bias, ZT overestimation, interpolation and extrapolation error, and digitization noise, and excludes inconsistent samples from the dataset. teMatDb272 and the Sc-ZT filtering framework offer a robust dataset for data-driven and machine-learning-based materials design, device modeling, and future thermoelectric research.