# Efficient autonomous material search method combining ab initio calculations, autoencoder, and multi-objective Bayesian optimization

https://mdr.nims.go.jp/datasets/fb204c65-1f81-4824-9159-bdf37fbab4d7

## File

- [Efficient autonomous material search method combining ab initio calculations autoencoder and multi objective Bayesian optimization.pdf](https://mdr.nims.go.jp/filesets/0a920f2b-8323-4bcc-bcf4-8d6f0c32e98f/download) ([Detail](https://mdr.nims.go.jp/filesets/0a920f2b-8323-4bcc-bcf4-8d6f0c32e98f.md))

## Id

fb204c65-1f81-4824-9159-bdf37fbab4d7

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-02-06T02:37:44.378682Z

## Updated at

2024-01-05T13:14:05.940240Z

## Published at

2023-02-07T02:04:39.108746Z

## Doi



## First published url

https://doi.org/10.1080/27660400.2022.2123263

## Date published

2022-12-31

## Recorded date published

2022-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Efficient autonomous material search method combining ab initio calculations,
    autoencoder, and multi-objective Bayesian optimization
  title_type: original
  lang: en

## Description

- description: Autonomous material search systems that combine ab initio calculations
    and Bayesian optimization are very promising for exploring huge material spaces.
    Setting up an appropriate material search space is necessary for efficient autonomous
    material search. However, performing the autonomous search within the material
    space set up using the prepared descriptors is not sufficient to obtain an efficient
    search, which can be achieved by prioritizing specific descriptors and properties.
    Here, a material search system that can autonomously search the huge material
    space while performing multi-objective optimization that considers similarities
    among elements and emphasizes specific descriptors is proposed. This system has
    been used for a material exploration of Heusler alloys. The system has successfully
    proposed several candidate materials with half-metallic properties. The proposed
    system is very versatile and can be applied to various properties and material
    systems.
  description_type: abstract
  lang: eng

## Creator

- name: Yuma Iwasaki
  role: author
  orcid: https://orcid.org/0000-0002-7117-277X
- name: Hwang Jaekyun
  role: author
  orcid: https://orcid.org/0000-0002-1792-0044
- name: Yuya Sakuraba
  role: author
  orcid: https://orcid.org/0000-0003-4618-9550
- name: Masato Kotsugi
  role: author
  orcid: https://orcid.org/0000-0002-4841-1808
- name: Yasuhiko Igarashi
  role: author
  orcid: https://orcid.org/0000-0003-1042-6657

## Contact agent



## Publisher



## Managing organization



## Keyword

- subject: autonomous materials search
  schema: not_defined
- subject: ab-initio
  schema: not_defined
- subject: Machine learning
  schema: not_defined

## Rights



## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: 'Science and Technology of Advanced Materials: Methods'
  issn: '27660400'
  volume: '2'
  issue: '1'
  start_page: 365
  end_page: 371

## Conference



## Related item



## Funding



## 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: 0a920f2b-8323-4bcc-bcf4-8d6f0c32e98f
  filename: Efficient autonomous material search method combining ab initio calculations
    autoencoder and multi objective Bayesian optimization.pdf
  content_type: application/pdf
  size: 5552008
  md5: 6bcb5241d85820eee31f8a968cda9c41

## Thumbnail

fileset_id: 0a920f2b-8323-4bcc-bcf4-8d6f0c32e98f
filename: Efficient autonomous material search method combining ab initio calculations
  autoencoder and multi objective Bayesian optimization.pdf