# Improving efficiency of autonomous material search via transfer learning from nontarget properties

https://mdr.nims.go.jp/datasets/1bdec3f6-50ca-4eee-acfe-12d1ca6e53b9

## File

- [Improving efficiency of autonomous material search via transfer learning from nontarget properties (2).pdf](https://mdr.nims.go.jp/filesets/115eb31f-0de2-4346-ba62-6df7b51f786f/download) ([Detail](https://mdr.nims.go.jp/filesets/115eb31f-0de2-4346-ba62-6df7b51f786f.md))

## Id

1bdec3f6-50ca-4eee-acfe-12d1ca6e53b9

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-10-26T07:36:30.888228Z

## Updated at

2024-01-05T13:11:31.928112Z

## Published at

2023-10-27T04:30:13.826239Z

## Doi



## First published url

https://doi.org/10.1080/27660400.2023.2254202

## Date published

2023-12-31

## Recorded date published

2023-12-31

## Resource type

journal_article

## Manuscript type

vor

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## Title

- title: Improving efficiency of autonomous material search via transfer learning
    from nontarget properties
  title_type: original
  lang: en

## Description

- description: Recently, autonomous material search methods combining machine learning
    and experiments/simulations have become indispensable for exploring the extremely
    vast material exploration space. However, conventional autonomous material search
    methods focus solely on target material properties and their descriptors, leaving
    room for improvement in search efficiency. More efficient autonomous material
    search can be realized by utilizing information on nontarget properties that are
    dormant in databases. Here, we propose a novel method for autonomous material
    search using transfer learning and an ensemble neural network. This method can
    perform an autonomous material search to optimize the target properties while
    transferring information on nontarget properties. To demonstrate the usefulness
    of this method, we applied it to search for ternary magnetic alloys with a high
    Curie temperature while transferring information on magnetic moment and spin polarization.
    Results indicate that the proposed method improves the efficiency of autonomous
    material search, particularly in the early stages of the search process.
  description_type: abstract
  lang: eng

## Creator

- name: Jaekyun Hwang
  role: author
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Yuma Iwasaki
  role: author
  orcid: https://orcid.org/0000-0002-7117-277X
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher

organization: Informa UK Limited

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## Keyword

- subject: Machine learning
  schema: not_defined

## Rights

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

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## Journal

- title: 'Science and Technology of Advanced Materials: Methods'
  issn: '27660400'
  volume: '3'
  issue: '1'
  start_page: 2254202
  end_page: 2254202

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## Fileset

- id: 115eb31f-0de2-4346-ba62-6df7b51f786f
  filename: Improving efficiency of autonomous material search via transfer learning
    from nontarget properties (2).pdf
  content_type: application/pdf
  size: 4443264
  md5: 139dc3f540d32d5a4ccca1b61eb6abf7

## Thumbnail

fileset_id: 115eb31f-0de2-4346-ba62-6df7b51f786f
filename: Improving efficiency of autonomous material search via transfer learning
  from nontarget properties (2).pdf