# Exploring utilization of generative AI for research and education in data-driven materials science

https://mdr.nims.go.jp/datasets/e5781a6b-ba6c-476e-9af1-143a9557c66a

## Files

- [Exploring utilization of generative AI for research and education in data-driven materials science.pdf](https://mdr.nims.go.jp/filesets/f4879026-fd8f-4cf8-aa9f-3acfbfdd7bef/download) ([Detail](https://mdr.nims.go.jp/filesets/f4879026-fd8f-4cf8-aa9f-3acfbfdd7bef.md))

## Id

e5781a6b-ba6c-476e-9af1-143a9557c66a

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-10-01T20:28:01.905708Z

## Updated at

2025-10-02T03:30:17.881603Z

## Published at

2025-10-02T03:20:20.410508Z

## Doi



## First published url

https://doi.org/10.1080/27660400.2025.2535956

## Date published

2025-12-31

## Recorded date published

2025-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Exploring utilization of generative AI for research and education in data-driven
    materials science
  title_type: original
  lang: en

## Description

- description: Generative AI has recently had a profound impact on various fields,
    including daily life, research, and education. To explore its efficient utilization
    in data-driven materials science, we organized a hackathon – AIMHack2024—in July
    2024. In this hackathon, researchers from fields such as materials science, information
    science, bioinformatics, and condensed matter physics worked together to explore
    how generative AI can facilitate research and education. Based on the results
    of the hackathon, this paper presents topics related to (1) conducting AI-assisted
    software trials, (2) building AI tutors for software, and (3) developing GUI applications
    for software. While generative AI continues to evolve rapidly, this paper provides
    an early record of its application in data-driven materials science and highlights
    strategies for integrating AI into research and education.
  description_type: abstract
  lang: und

## Creator

- name: Takahiro Misawa
  role: author
  orcid: https://orcid.org/0000-0001-6799-6672
- name: Ai Koizumi
  role: author
- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X
- name: Kazuyoshi Yoshimi
  role: author

## Contact agent



## Publisher

organization: Informa UK Limited

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## Keyword

- subject: generative AI
  schema: not_defined

## Rights

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

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## Embargo



## Journal

- title: 'Science and Technology of Advanced Materials: Methods'
  issn: '27660400'
  volume: '5'
  issue: '1'
  article_number: '2535956'

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## Specimen



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## Fileset

- id: f4879026-fd8f-4cf8-aa9f-3acfbfdd7bef
  filename: Exploring utilization of generative AI for research and education in data-driven
    materials science.pdf
  content_type: application/pdf
  size: 3088628
  md5: 03d8bce5caa76bf2f48582c8b8fe9bd0

## Thumbnail

fileset_id: f4879026-fd8f-4cf8-aa9f-3acfbfdd7bef
filename: Exploring utilization of generative AI for research and education in data-driven
  materials science.pdf