Molecular dynamics simulations of heat transport using machine-learned potentials: A mini review and tutorial on GPUMD with neuroevolution potentials
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916222092705792 |
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| author | Dong, Haikuan Shi, Yongbo Ying, Penghua Xu, Ke Liang, Ting Wang, Yanzhou Zeng, Zezhu Wu, Xin Zhou, Wenjiang Xiong, Shiyun Chen, Shunda Fan, Zheyong |
| author_facet | Dong, Haikuan Shi, Yongbo Ying, Penghua Xu, Ke Liang, Ting Wang, Yanzhou Zeng, Zezhu Wu, Xin Zhou, Wenjiang Xiong, Shiyun Chen, Shunda Fan, Zheyong |
| contents | 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 (NEPs) 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16249 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Molecular dynamics simulations of heat transport using machine-learned potentials: A mini review and tutorial on GPUMD with neuroevolution potentials Dong, Haikuan Shi, Yongbo Ying, Penghua Xu, Ke Liang, Ting Wang, Yanzhou Zeng, Zezhu Wu, Xin Zhou, Wenjiang Xiong, Shiyun Chen, Shunda Fan, Zheyong Materials Science Statistical Mechanics Computational Physics 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 (NEPs) 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. |
| title | Molecular dynamics simulations of heat transport using machine-learned potentials: A mini review and tutorial on GPUMD with neuroevolution potentials |
| topic | Materials Science Statistical Mechanics Computational Physics |
| url | https://arxiv.org/abs/2401.16249 |