# Enhancing thermoelectric efficiency of GeTe through process improvement via active learning assisted by Bayesian optimisation

https://mdr.nims.go.jp/datasets/1ab3ec9b-d053-4d99-801f-099313649483

## Files

- [Enhancing thermoelectric efficiency of GeTe through process improvement via active learning assisted by Bayesian optimisation.pdf](https://mdr.nims.go.jp/filesets/9c9ac2ab-cf62-402a-9c88-0db04cbed94f/download) ([Detail](https://mdr.nims.go.jp/filesets/9c9ac2ab-cf62-402a-9c88-0db04cbed94f.md))
- [tstm_a_2545174_sm4025.pdf](https://mdr.nims.go.jp/filesets/492d6345-f460-45cc-a3c3-e25c440bee45/download) ([Detail](https://mdr.nims.go.jp/filesets/492d6345-f460-45cc-a3c3-e25c440bee45.md))

## Id

1ab3ec9b-d053-4d99-801f-099313649483

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-12-09T05:21:26.719316Z

## Updated at

2025-12-09T23:30:23.955858Z

## Published at

2025-12-09T23:23:46.287529Z

## Doi



## First published url

https://doi.org/10.1080/27660400.2025.2545174

## Date published

2025-12-31

## Recorded date published

2025-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Enhancing thermoelectric efficiency of GeTe through process improvement via
    active learning assisted by Bayesian optimisation
  title_type: original
  lang: en

## Description

- description: In this work, we use a melt process in a vacuum-sealed quartz tube
    followed by spark plasma sintering to synthesize GeTe compounds. Through the application
    of active learning with Bayesian optimization, we systematically explore the process
    parameters to enhance the thermoelectric figure of merit (zT) to achieve a value
    of zT = 1.14 at 700 K. The results demonstrate an improvement of 25% over the
    equivalent 2-step process, the same efficiency (within 5%) as the 3-step process
    while drastically reducing processing time by over 90%, and this within 24 experiments
    out of more than 18 billion possible combinations. This illustrates the potential
    of the utilization of GeTe in mid-temperature range applications, and the effectiveness
    of artificial intelligence assistance to improve materials' properties in the
    tremendously vast process parameters space. This study is intended to be a textbook
    case displaying the effectiveness of active learning and Bayesian optimization
    at performing a full process optimization for enhancing the performance of a material
    with minimum experimental trials, time and cost.
  description_type: abstract
  lang: und

## Creator

- name: Florent Pawula
  role: author
  orcid: https://orcid.org/0000-0002-2641-0521
- name: Mathias Hamitouche
  role: author
- name: Linda Abbassi
  role: author
- name: Agathe Bajan
  role: author
- name: Cédric Bourgès
  role: author
- name: Takao Mori
  role: author
  orcid: https://orcid.org/0000-0003-2682-1846
  organization: National Institute for Materials Science
- name: Jean-François Halet
  role: author
- name: David Berthebaud
  role: author
- name: Guillaume Lambard
  role: author
  orcid: https://orcid.org/0000-0003-0275-4079
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: Informa UK Limited

## Managing organization



## Keyword

- subject: Material process
  schema: not_defined
- subject: bayesian optimisation
  schema: not_defined
- subject: active learning
  schema: not_defined
- subject: chalcogenides
  schema: not_defined
- subject: thermoelectrics
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: 'Science and Technology of Advanced Materials: Methods'
  issn: '27660400'
  volume: '5'
  issue: '1'
  article_number: '2545174'

## Conference



## Related item



## Funding

- identifier: PE20752
  funder_name: JSPS
- funder_name: CNRS
- funder_name: NIMS

## 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: 9c9ac2ab-cf62-402a-9c88-0db04cbed94f
  filename: Enhancing thermoelectric efficiency of GeTe through process improvement
    via active learning assisted by Bayesian optimisation.pdf
  content_type: application/pdf
  size: 5005619
  md5: 00e254730835ae93289bccda5593d29f
- id: 492d6345-f460-45cc-a3c3-e25c440bee45
  filename: tstm_a_2545174_sm4025.pdf
  content_type: application/pdf
  size: 2216974
  md5: '068fc0288c990320836447dffcaa7fb7'

## Thumbnail

fileset_id: 9c9ac2ab-cf62-402a-9c88-0db04cbed94f
filename: Enhancing thermoelectric efficiency of GeTe through process improvement
  via active learning assisted by Bayesian optimisation.pdf