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

https://mdr.nims.go.jp/datasets/6e7adfcb-29f9-454c-a9c1-4c1642fc0aac

## File

- [npj Computational Materials---SeeBand A highly efficient, interactive tool for analyzing electronic transport data.pdf](https://mdr.nims.go.jp/filesets/2d142859-0ebc-4de7-9a9c-58ae132ec339/download) ([Detail](https://mdr.nims.go.jp/filesets/2d142859-0ebc-4de7-9a9c-58ae132ec339.md))

## Id

6e7adfcb-29f9-454c-a9c1-4c1642fc0aac

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-11-08T16:01:15.948204Z

## Updated at

2025-11-10T07:30:45.905524Z

## Published at

2025-11-10T07:25:05.909337Z

## Doi



## First published url

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

## Date published

2025-06-07

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: 'SeeBand: a highly efficient, interactive tool for analyzing electronic transport
    data'
  title_type: original
  lang: en

## Description

- description: "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\r\n 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."
  description_type: abstract
  lang: und

## Creator

- name: Michael Parzer
  role: author
- name: Alexander Riss
  role: author
- name: Fabian Garmroudi
  role: author
- name: Johannes de Boor
  role: author
- name: Takao Mori
  role: author
  orcid: https://orcid.org/0000-0003-2682-1846
  organization: National Institute for Materials Science
- name: Ernst Bauer
  role: author

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

## Managing organization



## Keyword

- subject: thermoelectric
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by-nc-nd/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: npj Computational Materials
  issn: '20573960'
  volume: '11'
  issue: '1'
  article_number: '171'

## Conference



## Related item



## Funding

- identifier: JPMJMI19A1
  funder_name: JST Mirai
- identifier: JPMJMI19A1
  funder_name: JST Mirai
- identifier: JPMJMI19A1
  funder_name: JST Mirai
- identifier: JPMJMI19A1
  funder_name: JST Mirai
- identifier: JPMJMI19A1
  funder_name: JST Mirai

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: 2d142859-0ebc-4de7-9a9c-58ae132ec339
  filename: npj Computational Materials---SeeBand A highly efficient, interactive
    tool for analyzing electronic transport data.pdf
  content_type: application/pdf
  size: 1483812
  md5: c8003485e83c52db1373379aa3ce22ed

## Thumbnail

fileset_id: 2d142859-0ebc-4de7-9a9c-58ae132ec339
filename: npj Computational Materials---SeeBand A highly efficient, interactive tool
  for analyzing electronic transport data.pdf