# Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations

https://mdr.nims.go.jp/datasets/204eb1aa-8754-4c07-8247-66db101cecfb

## File

- [s41524-026-02033-w.pdf](https://mdr.nims.go.jp/filesets/b05d68f2-dfcb-4365-84ac-765fd3e43767/download) ([Detail](https://mdr.nims.go.jp/filesets/b05d68f2-dfcb-4365-84ac-765fd3e43767.md))

## Id

204eb1aa-8754-4c07-8247-66db101cecfb

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-04-15T01:39:00.461106Z

## Updated at

2026-05-11T23:46:56.614867Z

## Published at

2026-05-12T03:26:59.389139Z

## Doi



## First published url

https://doi.org/10.1038/s41524-026-02033-w

## Date published

2026-04-13

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Database and deep-learning scalability of anharmonic phonon properties by
    automated brute-force first-principles calculations
  title_type: original
  lang: en

## Description

- description: Understanding the anharmonic phonon properties of crystal compounds
    -- such as phonon lifetimes and thermal conductivities -- is essential for investigating
    and optimizing their thermal transport behaviors. These properties also impact
    optical, electronic, and magnetic characteristics through interactions between
    phonons and other quasiparticles and fields. In this study, we develop an automated
    first-principles workflow to calculate anharmonic phonon properties and build
    a comprehensive database encompassing more than 6,000 inorganic compounds. Utilizing
    this dataset, we train a graph neural network model to predict thermal conductivity
    values and spectra from structural parameters, demonstrating a scaling law in
    which prediction accuracy improves with increasing training data size. High-throughput
    screening with the model enables the identification of materials exhibiting extreme
    thermal conductivities -- both high and low. The resulting database offers valuable
    insights into the anharmonic behavior of phonons, thereby accelerating the design
    and development of advanced functional materials.
  description_type: abstract
  lang: und

## Creator

- name: Masato Ohnishi
  role: author
- name: Tianqi Deng
  role: author
- name: Pol Torres
  role: author
- name: Zhihao Xu
  role: author
- name: Terumasa Tadano
  role: author
  orcid: https://orcid.org/0000-0002-8132-2161
- name: Haoming Zhang
  role: author
- name: Wei Nong
  role: author
- name: Masatoshi Hanai
  role: author
- name: Zeyu Wang
  role: author
- name: Michimasa Morita
  role: author
- name: Zhiting Tian
  role: author
- name: Ming Hu
  role: author
- name: Xiulin Ruan
  role: author
- name: Ryo Yoshida
  role: author
- name: Toyotaro Suzumura
  role: author
- name: Lucas Lindsay
  role: author
- name: Alan J. H. McGaughey
  role: author
- name: Tengfei Luo
  role: author
- name: Kedar Hippalgaonkar
  role: author
- name: Junichiro Shiomi
  role: author

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

## Managing organization



## Keyword

- subject: Thermal conductivity
  schema: not_defined
- subject: Phonon
  schema: not_defined
- subject: First-principles calculation
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: npj Computational Materials
  issn: '20573960'
  volume: '12'
  issue: '1'
  article_number: '150'

## Conference



## Related item



## Funding

- funder_name: National Natural Science Foundation of China
- funder_name: U.S. Department of Energy
- funder_name: Agency for Science, Technology and Research
- funder_name: Japan Society for the Promotion of Science
- funder_name: Japan Science and Technology Agency

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## Specimen



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## Fileset

- id: b05d68f2-dfcb-4365-84ac-765fd3e43767
  filename: s41524-026-02033-w.pdf
  content_type: application/pdf
  size: 2504603
  md5: 778061caeb2e9530f3f21c7178a5c181

## Thumbnail

fileset_id: b05d68f2-dfcb-4365-84ac-765fd3e43767
filename: s41524-026-02033-w.pdf