<div class="csl-bib-body">
<div class="csl-entry">Parzer, M., Riss, A., Garmroudi, F., de Boor, J., Mori, T., & Bauer, E. (2025). SeeBand: a highly efficient, interactive tool for analyzing electronic transport data. <i>Npj Computational Materials</i>, <i>11</i>(1), Article 171. https://doi.org/10.1038/s41524-025-01645-y</div>
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dc.identifier.issn
2057-3960
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/223846
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dc.description.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.
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dc.description.sponsorship
Office of Research Contract Department of Contract Japan Science and Technology Agency (JST)
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dc.language.iso
en
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dc.publisher
NATURE PORTFOLIO
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dc.relation.ispartof
npj Computational Materials
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dc.subject
Thermoelectricity
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dc.subject
Electronic transport
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dc.subject
parabolic band model
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dc.subject
modeling
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dc.subject
least squares fits
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dc.title
SeeBand: a highly efficient, interactive tool for analyzing electronic transport data
