# Performance of uncertainty-based active learning for efficient approximation of black-box functions in materials science

https://mdr.nims.go.jp/datasets/0dc4e0f4-e452-4263-b1ea-841861ce53f4

## File

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## Id

0dc4e0f4-e452-4263-b1ea-841861ce53f4

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-11-14T07:28:19.267313Z

## Updated at

2024-11-21T07:35:14.560758Z

## Published at

2024-11-21T07:35:15.090051Z

## Doi



## First published url

https://doi.org/10.1038/s41598-024-76800-4

## Date published

2024-11-06

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Performance of uncertainty-based active learning for efficient approximation
    of black-box functions in materials science
  title_type: original
  lang: en

## Description

- description: Obtaining a fine approximation of a black-box function is important
    for understanding and evaluating innovative materials. Active learning aims to
    improve the approximation of black-box functions with fewer training data. In
    this study, we investigate whether active learning based on uncertainty sampling
    enables the efficient approximation of black-box functions in regression tasks
    using various material databases. In cases where the inputs are provided uniformly
    and defined in a relatively low-dimensional space, the liquidus surfaces of the
    ternary systems are the focus. The results show that uncertainty-based active
    learning can produce a better black-box function with higher prediction accuracy
    than that by random sampling. Furthermore, in cases in which the inputs are distributed
    discretely and unbalanced in a high-dimensional feature space, datasets extracted
    from materials databases for inorganic materials, small molecules, and polymers
    are addressed, and uncertainty-based active learning is occasionally inefficient.
    Based on the dependency on the material descriptors, active learning tends to
    produce a better black-box functions than random sampling when the dimensions
    of the descriptor are small. The results indicate that active learning is occasionally
    inefficient in obtaining a better black-box function in materials science.
  description_type: abstract
  lang: und

## Creator

- name: Ai Koizumi
  role: author
- name: Guillaume Deffrennes
  role: author
- name: Kei Terayama
  role: author
- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

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## Keyword

- subject: active learning
  schema: not_defined
- subject: black-box function
  schema: not_defined
- subject: materials informatics
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Scientific Reports
  issn: '20452322'
  volume: '14'
  article_number: '27019'

## Conference



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## Funding

- identifier: 21H01008
  funder_name: Japan Society for the Promotion of Science
- identifier: JPMJCR2234
  funder_name: Core Research for Evolutional Science and Technology
- identifier: JPMJCR2234
  funder_name: Core Research for Evolutional Science and Technology
- identifier: JPMXP1020230120
  funder_name: Ministry of Education, Culture, Sports, Science and Technology

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## Fileset

- id: daf47979-b8d4-467d-9c7b-c1b4cd932704
  filename: s41598-024-76800-4 (1).pdf
  content_type: application/pdf
  size: 2385703
  md5: 0cf1ed02af4d3123fa67cddb71b11901

## Thumbnail

fileset_id: daf47979-b8d4-467d-9c7b-c1b4cd932704
filename: s41598-024-76800-4 (1).pdf