2024/09/10 by Michael Parzer, Alexander Riss, Parzer, Michael +9
Engineering · #Computational Physics (physics.comp-ph) #Data Analysis #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Statistics and Probability (physics.data-an) #Traffic Prediction and Management Techniques
paper · pdf · doi:10.48550/arxiv.2409.06261
openalex publication_date 2024/09/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Linking the fundamental physics of band structure and scattering theory with macroscopic features such as measurable bulk thermoelectric transport properties is indispensable to a thorough understanding of transport phenomena and ensures more targeted and efficient experimental research. Here, we introduce SeeBand, a highly efficient and interactive fitting tool based on Boltzmann transport theory. A fully integrated user interface and visualization tool enable real-time comparison and connection between the electronic band structure (EBS) and microscopic transport properties. It allows simultaneous analysis of data for the Seebeck coefficient S, resistivity ρ and Hall coefficient RH to identify suitable EBS models and extract the underlying microscopic material parameters and additional information from the model. Crucially, the EBS can be obtained by directly fitting the temperature-dependent properties of a single sample, which goes beyond previous approaches that look into doping dependencies. Finally, the combination of neural-network-assisted initial guesses and an efficient subsequent fitting routine allows for a rapid processing of big datasets, facilitating high-throughput analyses to identify underlying, yet undiscovered dependencies, thereby guiding material design.