2025/04/01 by Zhenyao Fang, Fang, Zhenyao, Ting-Wei Hsu +3 · 2 citations
Materials Science · #Advanced Thermoelectric Materials and Devices #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Thermal properties of materials
paper · pdf · doi:10.48550/arxiv.2504.00771
openalex publication_date 2025/04/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The discovery and design of functional materials for energy harvesting and electronic applications require accurate predictions of their optical and transport properties. While several existing databases contain the first-order optical properties and the electron transport properties calculated from high-throughput first-principles calculations, the amount of material entries is often limited and those functional properties are often reported in scalar form. Comprehensive databases for the tensorial properties still remain inadequate, which prevents from capturing the anisotropic effect in materials and the development of advanced machine learning models that incorporate the space group symmetry of materials. Therefore, in this work we present the largest-to-date database of tensorial optical properties (optical conductivity, shift current) and the database of tensorial transport properties (electrical conductivity, thermal conductivity, Seebeck coefficient, thermoelectric figure of merit zT) for 7301 materials, calculated from the Wannier function method. The quality of the Wannier functions were validated by the maximal spread of the Wannier functions and by the comparison with the band structures from first-principles calculations, ensuring the accuracy of the calculated properties. These results contribute to the systematic study the functional properties for diverse materials and can benefit future data-driven discovery of candidate materials for optoelectronic and thermoelectric applications.