# AIPHAD, an active learning web application for visual understanding of phase diagrams

https://mdr.nims.go.jp/datasets/1473a8e6-5355-4091-bb8a-ab059bb6ed17

## File

- [s43246-024-00580-7.pdf](https://mdr.nims.go.jp/filesets/9a861775-e014-4360-8185-7b6c9488d7ea/download) ([Detail](https://mdr.nims.go.jp/filesets/9a861775-e014-4360-8185-7b6c9488d7ea.md))

## Id

1473a8e6-5355-4091-bb8a-ab059bb6ed17

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-08-09T21:38:57.107587Z

## Updated at

2024-08-26T07:30:23.732833Z

## Published at

2024-08-26T07:30:23.844274Z

## Doi



## First published url

https://doi.org/10.1038/s43246-024-00580-7

## Date published

2024-07-31

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: AIPHAD, an active learning web application for visual understanding of phase
    diagrams
  title_type: original
  lang: en

## Description

- description: 'Phase diagrams provide considerable information that is vital for
    materials exploration. However, the determination of multidimensional phase diagrams
    typically requires a significant investment of time, cost, and human resources
    owing to the necessity of numerous experiments or simulations. Machine learning
    and artificial intelligence techniques present a viable solution for expediting
    phase diagrams investigations. Additionally, effective visualization is critical
    for understanding phase diagrams. This study reports the development of AIPHAD
    (Artificial Intelligence technique for PHAse Diagram), an open-source web application
    to assist in the investigation and visual understanding of phase diagrams using
    active learning. AIPHAD employs PDC (Phase Diagram Construction) algorithm, which
    operates on the principle of uncertainty sampling in active learning. The AIPHAD
    application facilitates the examination of five diagram types: two-variable diagrams,
    three-variable diagrams, ternary sections, ternary phase diagrams, and quaternary
    sections. The efficacy of the application is demonstrated in the study of the
    Fe-Ti-Sn ternary system, where it efficiently identified the presence of the Heusler
    phase. The integration of machine learning tools with traditional materials science
    approaches showcased in this study has the potential to drive groundbreaking advancements
    in materials exploration and discovery.'
  description_type: abstract
  lang: und

## Creator

- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X
  organization: National Institute for Materials Science
- name: Haruhiko Morito
  role: author
- name: Guillaume Deffrennes
  role: author
  organization: National Institute for Materials Science
- name: Masanobu Naito
  role: author
  orcid: https://orcid.org/0000-0001-7198-819X
  organization: National Institute for Materials Science
- name: Yoshitaro Nose
  role: author
- name: Taichi Abe
  role: author
  orcid: https://orcid.org/0000-0002-5065-0939
  organization: National Institute for Materials Science
- name: Kei Terayama
  role: author

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

## Managing organization



## Keyword

- subject: phase diagram
  schema: not_defined
- subject: artificial intelligence
  schema: not_defined
- subject: active learning
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Communications Materials
  issn: '26624443'
  volume: '5'
  issue: '1'
  article_number: '139'

## Conference



## Related item



## Funding

- identifier: JPMJCR17J2
  funder_name: MEXT | Japan Science and Technology Agency
- identifier: JPMJCR19J1
  funder_name: MEXT | Japan Science and Technology Agency
- identifier: JPMJCR19J1
  funder_name: MEXT | Japan Science and Technology Agency
- identifier: JPMJCR19J3
  funder_name: MEXT | Japan Science and Technology Agency
- identifier: JPMJCR19J1
  funder_name: MEXT | Japan Science and Technology Agency

## 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: 9a861775-e014-4360-8185-7b6c9488d7ea
  filename: s43246-024-00580-7.pdf
  content_type: application/pdf
  size: 3261225
  md5: ad3158b0515c21b001072ce36f64df8f

## Thumbnail

fileset_id: 9a861775-e014-4360-8185-7b6c9488d7ea
filename: s43246-024-00580-7.pdf