# On-the-fly training of polynomial machine learning potentials in computing lattice thermal conductivity

https://mdr.nims.go.jp/datasets/4f3f2aef-db8b-4247-b8ca-82a7740d6c6c

## File

- [2401.17531v3.pdf](https://mdr.nims.go.jp/filesets/f36321b1-2a03-4bf0-b19d-5d80b4e23c22/download) ([Detail](https://mdr.nims.go.jp/filesets/f36321b1-2a03-4bf0-b19d-5d80b4e23c22.md))

## Id

4f3f2aef-db8b-4247-b8ca-82a7740d6c6c

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-08-20T01:25:36.464431Z

## Updated at

2024-08-29T07:30:19.731382Z

## Published at

2024-08-29T07:30:20.218256Z

## Doi



## First published url

https://doi.org/10.1063/5.0211296

## Date published

2024-06-07

## Recorded date published

2024-6-7

## Resource type

journal_article

## Manuscript type

authors_original

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## Title

- title: On-the-fly training of polynomial machine learning potentials in computing
    lattice thermal conductivity
  title_type: original
  lang: en

## Description

- description: The application of first-principles calculations for predicting lattice
    thermal conductivity (LTC) in crystalline materials, in conjunction with the linearized
    phonon Boltzmann equation, has gained increasing popularity. In this calculation,
    the determination of force constants through first-principles calculations is
    critical for accurate LTC predictions. For material exploration, performing first-principles
    LTC calculations in a high-throughput manner is now expected, although it requires
    significant computational resources. To reduce computational demands, we integrated
    polynomial machine learning potentials on-the-fly during the first-principles
    LTC calculations. This paper presents a systematic approach to first-principles
    LTC calculations. We designed and optimized an efficient workflow that integrates
    multiple modular software packages. We applied this approach to calculate LTCs
    for 103 compounds of the wurtzite, zincblende, and rocksalt types to evaluate
    the performance of the polynomial machine learning potentials in LTC calculations.
    We demonstrate a significant reduction in the computational resources required
    for the LTC predictions.
  description_type: abstract
  lang: und

## Creator

- name: Atsushi Togo
  role: author
  orcid: https://orcid.org/0000-0001-8393-9766
  organization: National Institute for Materials Science
- name: Atsuto Seko
  role: author

## Contact agent



## Publisher

organization: AIP Publishing

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## Keyword

- subject: Polynomial machine learning potential
  schema: not_defined
- subject: Lattice thermal conductivity calculation
  schema: not_defined

## Rights

- identifier: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: The Journal of Chemical Physics
  issn: '00219606'
  volume: '160'
  issue: '21'
  article_number: '211001'

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## Fileset

- id: f36321b1-2a03-4bf0-b19d-5d80b4e23c22
  filename: 2401.17531v3.pdf
  content_type: application/pdf
  size: 482414
  md5: 4d09100ea507d099e6c336bd27ca6fd5

## Thumbnail

fileset_id: f36321b1-2a03-4bf0-b19d-5d80b4e23c22
filename: 2401.17531v3.pdf