SeeBand: a highly efficient, interactive tool for analyzing electronic transport data
Michael Parzer, Alexander Riss, Fabian Garmroudi, Johannes de Boor, Takao Mori, E. Bauer
Abstract
SeeBand is an interactive tool for extracting microscopic material parameters by fitting temperature-dependent 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.
Topics & Concepts
Computer scienceHuman–computer interactionAdvanced Thermoelectric Materials and DevicesGas Sensing Nanomaterials and SensorsMachine Learning in Materials Science