# Dielectric tensor of perovskite oxides at finite temperature using equivariant graph neural network potentials

https://mdr.nims.go.jp/datasets/fef8f1f2-ab8f-4ccd-983a-7bc91aed1fb1

## File

- [Dielectric tensor of perovskite oxides at finite temperature using equivariant graph neural network potentials.pdf](https://mdr.nims.go.jp/filesets/7f93d597-9659-4c05-8728-ebb5fe85d82d/download) ([Detail](https://mdr.nims.go.jp/filesets/7f93d597-9659-4c05-8728-ebb5fe85d82d.md))

## Id

fef8f1f2-ab8f-4ccd-983a-7bc91aed1fb1

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-04-24T02:02:02.402384Z

## Updated at

2025-07-18T01:24:15.512915Z

## Published at

2025-04-24T03:26:37.545959Z

## Doi

https://doi.org/10.48505/nims.5451

## First published url

https://doi.org/10.1080/27660400.2025.2497254

## Date published

2025-12-31

## Recorded date published

2025-12-31

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: Dielectric tensor of perovskite oxides at finite temperature using equivariant
    graph neural network potentials
  title_type: original
  lang: en

## Description

- description: Atomistic simulations of properties of materials at finite temperatures
    are computationally demanding and require models that are more efficient than
    the ab initio approaches. Machine learning (ML) and artificial intelligence (AI)
    address this issue by enabling accurate models with close to ab initio accuracy.
    Here, we demonstrate the utility of ML models in capturing properties of realistic
    materials by performing finite temperature molecular dynamics simulations of perovskite
    oxides using a force field based on equivariant graph neural networks. The models
    demonstrate efficient learning from a small training dataset of energies, forces,
    stresses, and tensors of Born effective charges. We qualitatively capture the
    temperature dependence of the dielectric tensor and structural phase transitions
    in calcium titanate.
  description_type: abstract
  lang: en

## Creator

- name: Alex Kutana
  role: author
  organization: Nagoya University
- name: Koki Yoshimochi
  role: author
- name: Ryoji Asahi
  role: author

## Contact agent



## Publisher

organization: Taylor & Francis

## Managing organization



## Keyword

- subject: Graph neural network
  schema: not_defined
- subject: machine learning
  schema: not_defined
- subject: dielectrics
  schema: not_defined
- subject: perovskite oxides
  schema: not_defined
- subject: phase transitions
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: 'Science and Technology of Advanced Materials: Methods'
  issn: '27660400'
  volume: '5'
  article_number: '2497254 '

## 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: 7f93d597-9659-4c05-8728-ebb5fe85d82d
  filename: Dielectric tensor of perovskite oxides at finite temperature using equivariant
    graph neural network potentials.pdf
  content_type: application/pdf
  size: 1450294
  md5: aefd6d8fea965c2c87fb44c5fd862bfc

## Thumbnail

fileset_id: 7f93d597-9659-4c05-8728-ebb5fe85d82d
filename: Dielectric tensor of perovskite oxides at finite temperature using equivariant
  graph neural network potentials.pdf