# AI agents for automating materials research: a case study of crystal plasticity simulations

https://mdr.nims.go.jp/datasets/8dce5c43-c81d-4087-a9cf-8bd9b5b61125

## File

- [AI agents for automating materials research  a case study of crystal plasticity simulations.pdf](https://mdr.nims.go.jp/filesets/7620b7e3-0378-4a49-88d1-292caab40241/download) ([Detail](https://mdr.nims.go.jp/filesets/7620b7e3-0378-4a49-88d1-292caab40241.md))

## Id

8dce5c43-c81d-4087-a9cf-8bd9b5b61125

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-07-03T00:04:57.521333Z

## Updated at

2026-07-03T01:01:23.832178Z

## Published at

2026-07-03T03:30:35.954961Z

## Doi



## First published url

https://doi.org/10.1080/27660400.2026.2630445

## Date published

2026-12-31

## Recorded date published

2026-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: 'AI agents for automating materials research: a case study of crystal plasticity
    simulations'
  title_type: original
  lang: en

## Description

- description: "This study presents CrystalPlasticitySim, a multi-agent system that
    leverages large language models (LLMs) to automate complex workflows in crystal
    plasticity simulations. Traditional simulation processes require extensive expertise
    in materials science, software operation, and computational techniques, making
    them time-consuming and inaccessible to non-specialists. To address these challenges,
    our system integrates three collaborating AI agents—a Supervisor Agent, Simulation
    Agent, and Computational Assistant Agent—that autonomously handle task decomposition,
    input file generation, simulation execution, result extraction, and parameter
    optimization.\r\n\r\nUsing a case study on the anisotropic deformation behavior
    of Ni₃Al single crystals during cold rolling, we demonstrate that CrystalPlasticitySim
    can significantly reduce manual effort and improve efficiency. Tasks that previously
    required months of human work can be completed within hours through autonomous
    execution. The system also incorporates self-correction mechanisms, enabling it
    to detect and resolve common runtime errors without human intervention.\r\n\r\nFurthermore,
    we propose a four-level taxonomy of AI agents in materials simulation and position
    our system at the transition between task-solving and problem-solving agents.
    The results highlight the potential of AI-driven multi-agent systems to enhance
    reproducibility, accessibility, and efficiency in materials research workflows."
  description_type: abstract
  lang: und

## Creator

- name: Jiyi Yang
  role: author
  orcid: https://orcid.org/0009-0003-0213-1258
  organization: National Institute for Materials Science
- name: Yoshinao Kobayashi
  role: author
- name: Masahiko Demura
  role: author
  orcid: https://orcid.org/0000-0002-7308-3041
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: Informa UK Limited

## Managing organization



## Keyword

- subject: Multi-agent LLM
  schema: not_defined
- subject: Crystal plasticity simulation
  schema: not_defined
- subject: Automated simulation
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/
  date_licensed: 2026-04-02

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: 'Science and Technology of Advanced Materials: Methods'
  issn: '27660400'
  volume: '6'
  issue: '1'
  article_number: '2630445'

## Conference



## Related item



## Funding

- identifier: JPMXP1122684766
  funder_name: 'Ministry of Education, Culture, Sports, Science and Technology (MEXT)
    Program: Data Creation and Utilization-Type Material Research and Development
    Project'

## 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



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## Fileset

- id: 7620b7e3-0378-4a49-88d1-292caab40241
  filename: AI agents for automating materials research  a case study of crystal plasticity
    simulations.pdf
  content_type: application/pdf
  size: 3569460
  md5: 19d2d7bd1382cf457a491560f47b9070

## Thumbnail

fileset_id: 7620b7e3-0378-4a49-88d1-292caab40241
filename: AI agents for automating materials research  a case study of crystal plasticity
  simulations.pdf