# ALMLBO PIPELINE APPROACH APPLY TO MATERIAL PROCESS AND COMPOSITION OPTIMIZATION FOR ENERGY-SAVING APPLICATIONS

https://mdr.nims.go.jp/datasets/fd47b412-8c17-4970-bffa-342f35f62ca1

## File

- [Revised Manuscript-ActaMater_20240327.docx](https://mdr.nims.go.jp/filesets/77c75199-e31f-4bc1-bf8f-531ab7ad6804/download) ([Detail](https://mdr.nims.go.jp/filesets/77c75199-e31f-4bc1-bf8f-531ab7ad6804.md))

## Id

fd47b412-8c17-4970-bffa-342f35f62ca1

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-10-23T23:57:59.407936Z

## Updated at

2024-10-30T07:30:23.955953Z

## Published at

2024-10-30T07:30:25.192758Z

## Doi

https://doi.org/10.48505/nims.4910

## First published url



## Date published



## Recorded date published



## Resource type

conference_presentation

## Manuscript type

authors_original

## Collection



## Title

- title: ALMLBO PIPELINE APPROACH APPLY TO MATERIAL PROCESS AND COMPOSITION OPTIMIZATION
    FOR ENERGY-SAVING APPLICATIONS
  title_type: original
  lang: en

## Description

- description: "The active learning by machine learning and Bayesian optimization
    pipeline (ALMLBO) is a new tool rising in the experimental material development.
    The pipeline is a general framework that comprises: a “learning” step based on
    past experiments and during which a statistical modelization of a system {process
    parameters/composition, targeted properties} is attempted by using experimental
    data to train a machine learning model that capture relationships between parameters
    and properties; And an “active” step in which a set of experimental actions, derived
    from the learned model of the system, are performed and are supposed to bring
    the system closer to an objective. Bayesian optimization leverages this model-building
    to guide the choice of parameter sets. Multiple learning-acting cycles constitute
    an active learning pipeline. \r\nIn the present case, the use of the ALMLBO has
    been developed on a strategic material, the kesterite [3], to develop a process
    control and a fine composition adjustment as both key factor for obtaining a superior
    thermoelectric (TE) property. On the first hand, it can reduce the number of experiments
    required to find the ideal set of process parameters or composition tunning that
    improve TE properties and, on the second hand, propose statistical relationships
    between the process parameters and the targeted physical properties. Notably,
    the latter advantage supports the establishment of dependencies which could appear
    pertinent to the understanding of a physicochemical system like in the present
    study."
  description_type: abstract
  lang: eng

## Creator

- name: Cédric Bourgès
  role: author
  orcid: https://orcid.org/0000-0001-9056-0420
  organization: National Institute for Materials Science
  department: International Center for Young Scientists
  ror: https://ror.org/026v1ze26
- name: Guillaume Lambard
  role: author
  orcid: https://orcid.org/0000-0003-0275-4079
  organization: National Institute for Materials Science
  department: Center for Basic Research on Materials/Data-driven Materials Research
    Field/Data-driven Materials Design Group
  ror: https://ror.org/026v1ze26
- name: Naoki Sato
  role: author
  orcid: https://orcid.org/0000-0002-6429-0591
  organization: National Institute for Materials Science
  department: Research Center for Materials Nanoarchitectonics (MANA)/Nanomaterials
    Field/Thermal Energy Materials Group
  ror: https://ror.org/026v1ze26
- name: Makoto Tachibana
  role: author
  orcid: https://orcid.org/0000-0002-5907-5563
  organization: National Institute for Materials Science
  department: Research Center for Materials Nanoarchitectonics (MANA)/Nanomaterials
    Field/Thermal Energy Materials Group
  ror: https://ror.org/026v1ze26
- name: Satoshi Ishii
  role: author
  orcid: https://orcid.org/0000-0003-0731-8428
  organization: National Institute for Materials Science
  department: Research Center for Materials Nanoarchitectonics (MANA)/Nanomaterials
    Field/Optical Nanostructure Team
  ror: https://ror.org/026v1ze26
- name: Takao Mori
  role: author
  orcid: https://orcid.org/0000-0003-2682-1846
  organization: National Institute for Materials Science
  department: Research Center for Materials Nanoarchitectonics (MANA)/Nanomaterials
    Field/Thermal Energy Materials Group
  ror: https://ror.org/026v1ze26

## Contact agent



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## 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: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal



## Conference

name: THE 11th INTERNATIONAL WORKSHOP ON ADVANCED MATERIALS SCIENCE AND NANOTECHNOLOGY
start_date: 2024-09-22
end_date: 2024-09-25
identifier: https://iwamsn.ac.vn/

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## Fileset

- id: 77c75199-e31f-4bc1-bf8f-531ab7ad6804
  filename: Revised Manuscript-ActaMater_20240327.docx
  content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document
  size: 4037724
  md5: 9a8d2d1fec5c4cd74abf9e5029388997

## Thumbnail

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filename: Revised Manuscript-ActaMater_20240327.docx