---
_id: '15359'
abstract:
- lang: eng
  text: Molecular dynamics (MD) simulations play an important role in understanding
    and engineering heat transport properties of complex materials. An essential requirement
    for reliably predicting heat transport properties is the use of accurate and efficient
    interatomic potentials. Recently, machine-learned potentials (MLPs) have shown
    great promise in providing the required accuracy for a broad range of materials.
    In this mini-review and tutorial, we delve into the fundamentals of heat transport,
    explore pertinent MD simulation methods, and survey the applications of MLPs in
    MD simulations of heat transport. Furthermore, we provide a step-by-step tutorial
    on developing MLPs for highly efficient and predictive heat transport simulations,
    utilizing the neuroevolution potentials as implemented in the GPUMD package. Our
    aim with this mini-review and tutorial is to empower researchers with valuable
    insights into cutting-edge methodologies that can significantly enhance the accuracy
    and efficiency of MD simulations for heat transport studies.
acknowledgement: H.D. is supported by the Science Foundation from the Education Department
  of Liaoning Province (No. JYTMS20231613) and the Doctoral start-up Fund of Bohai
  University (No. 0523bs008). P.Y. is supported by the Israel Academy of Sciences
  and Humanities & Council for Higher Education Excellence Fellowship Program for
  International Postdoctoral Researchers. K.X. and T.L. acknowledge support from the
  National Key R&D Project from Ministry of Science and Technology of China (No. 2022YFA1203100),
  the Research Grants Council of Hong Kong (No. AoE/P-701/20), and RGC GRF (No. 14220022).
  Z.Z. acknowledges the European Union’s Horizon 2020 research and innovation programme
  under the Marie Skłodowska-Curie Grant Agreement No. 101034413. S.X. acknowledges
  financial support from the National Natural Science Foundation of China (NNSFC)
  (Grant No. 12174276).
article_number: '161101'
article_processing_charge: Yes (in subscription journal)
article_type: review
arxiv: 1
author:
- first_name: Haikuan
  full_name: Dong, Haikuan
  last_name: Dong
- first_name: Yongbo
  full_name: Shi, Yongbo
  last_name: Shi
- first_name: Penghua
  full_name: Ying, Penghua
  last_name: Ying
- first_name: Ke
  full_name: Xu, Ke
  last_name: Xu
- first_name: Ting
  full_name: Liang, Ting
  last_name: Liang
- first_name: Yanzhou
  full_name: Wang, Yanzhou
  last_name: Wang
- first_name: Zezhu
  full_name: Zeng, Zezhu
  id: 54a2c730-803f-11ed-ab7e-95b29d2680e7
  last_name: Zeng
- first_name: Xin
  full_name: Wu, Xin
  last_name: Wu
- first_name: Wenjiang
  full_name: Zhou, Wenjiang
  last_name: Zhou
- first_name: Shiyun
  full_name: Xiong, Shiyun
  last_name: Xiong
- first_name: Shunda
  full_name: Chen, Shunda
  last_name: Chen
- first_name: Zheyong
  full_name: Fan, Zheyong
  last_name: Fan
citation:
  ama: 'Dong H, Shi Y, Ying P, et al. Molecular dynamics simulations of heat transport
    using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution
    potentials. <i>Journal of Applied Physics</i>. 2024;135(16). doi:<a href="https://doi.org/10.1063/5.0200833">10.1063/5.0200833</a>'
  apa: 'Dong, H., Shi, Y., Ying, P., Xu, K., Liang, T., Wang, Y., … Fan, Z. (2024).
    Molecular dynamics simulations of heat transport using machine-learned potentials:
    A mini-review and tutorial on GPUMD with neuroevolution potentials. <i>Journal
    of Applied Physics</i>. AIP Publishing. <a href="https://doi.org/10.1063/5.0200833">https://doi.org/10.1063/5.0200833</a>'
  chicago: 'Dong, Haikuan, Yongbo Shi, Penghua Ying, Ke Xu, Ting Liang, Yanzhou Wang,
    Zezhu Zeng, et al. “Molecular Dynamics Simulations of Heat Transport Using Machine-Learned
    Potentials: A Mini-Review and Tutorial on GPUMD with Neuroevolution Potentials.”
    <i>Journal of Applied Physics</i>. AIP Publishing, 2024. <a href="https://doi.org/10.1063/5.0200833">https://doi.org/10.1063/5.0200833</a>.'
  ieee: 'H. Dong <i>et al.</i>, “Molecular dynamics simulations of heat transport
    using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution
    potentials,” <i>Journal of Applied Physics</i>, vol. 135, no. 16. AIP Publishing,
    2024.'
  ista: 'Dong H, Shi Y, Ying P, Xu K, Liang T, Wang Y, Zeng Z, Wu X, Zhou W, Xiong
    S, Chen S, Fan Z. 2024. Molecular dynamics simulations of heat transport using
    machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution
    potentials. Journal of Applied Physics. 135(16), 161101.'
  mla: 'Dong, Haikuan, et al. “Molecular Dynamics Simulations of Heat Transport Using
    Machine-Learned Potentials: A Mini-Review and Tutorial on GPUMD with Neuroevolution
    Potentials.” <i>Journal of Applied Physics</i>, vol. 135, no. 16, 161101, AIP
    Publishing, 2024, doi:<a href="https://doi.org/10.1063/5.0200833">10.1063/5.0200833</a>.'
  short: H. Dong, Y. Shi, P. Ying, K. Xu, T. Liang, Y. Wang, Z. Zeng, X. Wu, W. Zhou,
    S. Xiong, S. Chen, Z. Fan, Journal of Applied Physics 135 (2024).
date_created: 2024-05-05T22:01:03Z
date_published: 2024-04-28T00:00:00Z
date_updated: 2025-09-04T13:55:06Z
day: '28'
ddc:
- '530'
department:
- _id: BiCh
doi: 10.1063/5.0200833
ec_funded: 1
external_id:
  arxiv:
  - '2401.16249'
  isi:
  - '001215967400009'
file:
- access_level: open_access
  checksum: 4d6abb3ebe058ce8eebf4fc7e9cdda0d
  content_type: application/pdf
  creator: dernst
  date_created: 2024-05-13T08:07:44Z
  date_updated: 2024-05-13T08:07:44Z
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file_date_updated: 2024-05-13T08:07:44Z
has_accepted_license: '1'
intvolume: '       135'
isi: 1
issue: '16'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Preprint
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
  call_identifier: H2020
  grant_number: '101034413'
  name: 'IST-BRIDGE: International postdoctoral program'
publication: Journal of Applied Physics
publication_identifier:
  eissn:
  - 1089-7550
  issn:
  - 0021-8979
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
related_material:
  link:
  - relation: software
    url: https://gitlab.com/brucefan1983/nep-data
scopus_import: '1'
status: public
title: 'Molecular dynamics simulations of heat transport using machine-learned potentials:
  A mini-review and tutorial on GPUMD with neuroevolution potentials'
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 135
year: '2024'
...