摘要
SeeBand is an interactive tool for extracting microscopic material parameters by fitting temperaturedependent thermoelectric transport properties using Boltzmann transport theory.With real-time comparison between electronic band structures and transport data,it analyzes the Seebeck coefficient,resistivity,and Hall coefficient.Neural-network-assisted guesses and efficient fitting routines enable high-throughput processing of large datasets.SeeBand accelerates material design by allowing electronic band structure models to be derived directly from a single sample’s transport measurements.
基金
supported by the Japan Science and Technology Agency (JST) programs MIRAI, No. JPMJMI19A1. The authors thank Nikolas Reumann for fruitful discussions regarding the theoretical framework of SeeBand. We thank the anonymous reviewers for their constructive feedback, which significantly improved both the manuscript and the SeeBand software
The authors acknowledge TU Wien Bibliothek for financial support through its Open Access Funding Programme.
