Article SeeBand: a highly efficient, interactive tool for analyzing electronic transport data

Michael Parzer ; Alexander Riss ; Fabian Garmroudi ; Johannes de Boor ; Takao Mori SAMURAI ORCID (National Institute for Materials Science) ; Ernst Bauer

npj Computational Materials---SeeBand A highly efficient, interactive tool for analyzing electronic transport data.pdf
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Michael Parzer, Alexander Riss, Fabian Garmroudi, Johannes de Boor, Takao Mori, Ernst Bauer. SeeBand: a highly efficient, interactive tool for analyzing electronic transport data. npj Computational Materials. 2025, 11 (1), 171. https://doi.org/10.1038/s41524-025-01645-y

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(abstract)

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.

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Keyword: thermoelectric

Date published: 2025-06-07

Publisher: Springer Science and Business Media LLC

Journal:

  • npj Computational Materials (ISSN: 20573960) vol. 11 issue. 1 171

Funding:

  • JST Mirai JPMJMI19A1
  • JST Mirai JPMJMI19A1
  • JST Mirai JPMJMI19A1
  • JST Mirai JPMJMI19A1
  • JST Mirai JPMJMI19A1

Manuscript type: Publisher's version (Version of record)

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First published URL: https://doi.org/10.1038/s41524-025-01645-y

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Updated at: 2025-11-10 16:30:45 +0900

Published on MDR: 2025-11-10 16:25:05 +0900

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