{"intvolume":"       135","related_material":{"link":[{"url":"https://gitlab.com/brucefan1983/nep-data","relation":"software"}]},"language":[{"iso":"eng"}],"quality_controlled":"1","oa":1,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file_date_updated":"2024-05-13T08:07:44Z","file":[{"creator":"dernst","content_type":"application/pdf","date_updated":"2024-05-13T08:07:44Z","file_size":3240613,"relation":"main_file","access_level":"open_access","file_name":"2024_JourApplPhysics_Dong.pdf","date_created":"2024-05-13T08:07:44Z","success":1,"checksum":"4d6abb3ebe058ce8eebf4fc7e9cdda0d","file_id":"15382"}],"date_published":"2024-04-28T00:00:00Z","day":"28","publication_status":"published","publication":"Journal of Applied Physics","author":[{"last_name":"Dong","full_name":"Dong, Haikuan","first_name":"Haikuan"},{"last_name":"Shi","full_name":"Shi, Yongbo","first_name":"Yongbo"},{"first_name":"Penghua","full_name":"Ying, Penghua","last_name":"Ying"},{"first_name":"Ke","last_name":"Xu","full_name":"Xu, Ke"},{"first_name":"Ting","full_name":"Liang, Ting","last_name":"Liang"},{"full_name":"Wang, Yanzhou","last_name":"Wang","first_name":"Yanzhou"},{"first_name":"Zezhu","id":"54a2c730-803f-11ed-ab7e-95b29d2680e7","full_name":"Zeng, Zezhu","last_name":"Zeng"},{"full_name":"Wu, Xin","last_name":"Wu","first_name":"Xin"},{"full_name":"Zhou, Wenjiang","last_name":"Zhou","first_name":"Wenjiang"},{"last_name":"Xiong","full_name":"Xiong, Shiyun","first_name":"Shiyun"},{"first_name":"Shunda","last_name":"Chen","full_name":"Chen, Shunda"},{"full_name":"Fan, Zheyong","last_name":"Fan","first_name":"Zheyong"}],"doi":"10.1063/5.0200833","_id":"15359","volume":135,"external_id":{"arxiv":["2401.16249"]},"project":[{"name":"IST-BRIDGE: International postdoctoral program","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","call_identifier":"H2020","grant_number":"101034413"}],"issue":"16","publisher":"AIP Publishing","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."}],"year":"2024","ddc":["530"],"publication_identifier":{"issn":["0021-8979"],"eissn":["1089-7550"]},"oa_version":"Preprint","department":[{"_id":"BiCh"}],"article_type":"review","status":"public","ec_funded":1,"article_number":"161101","date_created":"2024-05-05T22:01:03Z","has_accepted_license":"1","citation":{"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>.","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>","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.","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).","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.","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>","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>."},"type":"journal_article","month":"04","date_updated":"2024-05-13T08:13:33Z","title":"Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials","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_processing_charge":"Yes (in subscription journal)","scopus_import":"1"}