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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Thermal properties dataset of high-emissivity ceramic materials generated with automated data extraction
Author(s) Taek Gyu Kwon, Yi Je Cho
On-Site Speaker (Planned) Taek Gyu Kwon
Abstract Scope High-emissivity ceramics are important for radiative thermal management at elevated temperatures. However, dedicated, machine-readable datasets remain scarce because thermophysical-property data are dispersed across scientific literature and reported under heterogeneous materials, processing, and measurement conditions. This data fragmentation impedes cross-study comparison and data-driven materials development. In this study, an automated literature-mining pipeline to generate a thermal-properties dataset for oxide and non-oxide high-emissivity ceramics was developed. The pipeline retrieves relevant publications, identifies material–property relations, and extracts composition, processing parameters, specimen form, density, porosity, heat capacity, thermal diffusivity, thermal conductivity, thermal expansion coefficient, and spectral or total emissivity. Extracted records are harmonized using standardized units and controlled terminology, linked to temperature, wavelength, atmosphere, and measurement method, and subjected to rule-based quality control and source-provenance tracking. The resulting structured dataset supports quantitative analysis and subsequent machine-learning models for property prediction and candidate-material screening.
Proceedings Inclusion? Planned:
Keywords Ceramics,

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