# Process optimization on kesterite-based ceramics for enhancing their thermoelectric performances assisted by active machine learning approach: A tool for metal-sulfide ceramics development

https://mdr.nims.go.jp/datasets/c288eb58-d410-4477-a2c2-852d332eb5f1

## File

- [Revised-SI-ActaMater 1_GLambard_20240801.docx](https://mdr.nims.go.jp/filesets/5c5266fd-192c-4147-abdb-d2d6e3052824/download) ([Detail](https://mdr.nims.go.jp/filesets/5c5266fd-192c-4147-abdb-d2d6e3052824.md))
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## Id

c288eb58-d410-4477-a2c2-852d332eb5f1

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-09-09T03:29:37.749381Z

## Updated at

2024-09-20T07:30:30.667082Z

## Published at

2024-09-20T07:30:30.750244Z

## Doi



## First published url

https://doi.org/10.1016/j.actamat.2024.120342

## Date published

2024-08-29

## Recorded date published

2024-12

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: 'Process optimization on kesterite-based ceramics for enhancing their thermoelectric
    performances assisted by active machine learning approach: A tool for metal-sulfide
    ceramics development'
  title_type: original
  lang: en

## Description

- description: The thermal process parameters are crucial in metal-sulfides ceramics
    as they affect significantly the resulting physico-chemical properties. In the
    present work, we investigated the sintering effect in the kesterite Cu2.125Zn0.875SnS4
    on its structural, microstructural, and thermoelectric (TE) properties to highlight
    the non-negligible contribution of the thermal process often ignored in metal-sulfide
    ceramics. For this purpose, we developed an approach combining data science with
    the conventional material experiment/theory approach which can be used as a tool
    to shortcut the time-consuming steps of TE material optimization. We confirmed
    that the optimization and control of the densification process is critical in
    unravelling the highest potential on metal sulfide TE ceramics with a non-negligible
    increase of its zT up to 60%. We propose a scientific tool, the synergic combination
    of active machine learning with conventional chemistry/theory approaches, to either
    identify the most proficient sintering process as well as the process to avoid
    the degradation of the metal-sulfide ceramic properties and thus in a shorten
    number of experiments. This approach can be extended not only to other metal-sulfide
    ceramics for thermoelectricity but also to other research fields.
  description_type: abstract
  lang: und

## Creator

- name: Cédric Bourgès
  role: author
  orcid: https://orcid.org/0000-0001-9056-0420
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Guillaume Lambard
  role: author
  orcid: https://orcid.org/0000-0003-0275-4079
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Naoki Sato
  role: author
  orcid: https://orcid.org/0000-0002-6429-0591
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Makoto Tachibana
  role: author
  orcid: https://orcid.org/0000-0002-5907-5563
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Satoshi Ishii
  role: author
  orcid: https://orcid.org/0000-0003-0731-8428
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Takao Mori
  role: author
  orcid: https://orcid.org/0000-0003-2682-1846
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: Kesterite
  schema: not_defined
- subject: Machine learning
  schema: not_defined
- subject: Process
  schema: not_defined
- subject: Thermoelectric
  schema: not_defined
- subject: Ceramic
  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: Acta Materialia
  issn: '13596454'
  volume: '281'
  article_number: '120342'

## Conference



## Related item



## Funding

- funder_name: JST-Mirai Program
- funder_name: Japan Society for the Promotion of Science

## 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



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