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<p><i>SeeBand</i> 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. <i>SeeBand</i> accelerates material design by allowing electronic band structure models to be derived directly from a single sample’s transport measurements.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月7日

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  • Michael Parzer,
  • Alexander Riss,
  • Fabian Garmroudi,
  • Johannes de Boor,
  • Takao Mori,
  • Ernst Bauer

摘要

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.