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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2507.17032 |
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| _version_ | 1866912497937678336 |
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| author | Wang, Yuxuan Lan, Guoqiang Chen, Huicong Song, Jun |
| author_facet | Wang, Yuxuan Lan, Guoqiang Chen, Huicong Song, Jun |
| contents | High-entropy pyrochlore oxides possess ultra-low thermal conductivity and excellent high-temperature phase stability, making them promising candidate for next-generation thermal barrier coating (TBC) materials. However, reliable predictive models for such complex and disordered systems remain challenging. Ab initio methods, although accurate in describing anharmonic phonon-phonon interactions, struggle to capture the strong inherent phonon-disorder scattering in high-entropy systems. Moreover, the limited simulation cell size, hundreds of atoms, cannot fully represent the configurational complexity of high-entropy phases. On the other hand, classical molecular dynamics (MD) simulations lack accurate and transferable interatomic potentials, particularly in multi-component systems like high-entropy ceramics. In this work, we employed Deep Potential Molecular Dynamics (DPMD) to predict the thermophysical and mechanical properties of rare-earth high-entropy pyrochlore oxide system. The deep-potential (DP) model is trained on a limited dataset from ab initio molecular dynamics (AIMD) calculations, enabling large-scale molecular dynamics simulations with on-the-fly potential evaluations. This model not only achieves high accuracy in reproducing ab initio results but also demonstrates strong generalizability, making it applicable to medium-entropy ceramics containing the same constituent elements. Our study successfully develops a deep potential model for rare-earth pyrochlore systems and demonstrates that the deep-learning-based potential method offers a powerful computational approach for designing high-entropy TBC materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17032 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Thermophysical and Mechanical Properties Prediction of Rear-earth High-entropy Pyrochlore Based on Deep-learning Potential Wang, Yuxuan Lan, Guoqiang Chen, Huicong Song, Jun Materials Science Computational Physics High-entropy pyrochlore oxides possess ultra-low thermal conductivity and excellent high-temperature phase stability, making them promising candidate for next-generation thermal barrier coating (TBC) materials. However, reliable predictive models for such complex and disordered systems remain challenging. Ab initio methods, although accurate in describing anharmonic phonon-phonon interactions, struggle to capture the strong inherent phonon-disorder scattering in high-entropy systems. Moreover, the limited simulation cell size, hundreds of atoms, cannot fully represent the configurational complexity of high-entropy phases. On the other hand, classical molecular dynamics (MD) simulations lack accurate and transferable interatomic potentials, particularly in multi-component systems like high-entropy ceramics. In this work, we employed Deep Potential Molecular Dynamics (DPMD) to predict the thermophysical and mechanical properties of rare-earth high-entropy pyrochlore oxide system. The deep-potential (DP) model is trained on a limited dataset from ab initio molecular dynamics (AIMD) calculations, enabling large-scale molecular dynamics simulations with on-the-fly potential evaluations. This model not only achieves high accuracy in reproducing ab initio results but also demonstrates strong generalizability, making it applicable to medium-entropy ceramics containing the same constituent elements. Our study successfully develops a deep potential model for rare-earth pyrochlore systems and demonstrates that the deep-learning-based potential method offers a powerful computational approach for designing high-entropy TBC materials. |
| title | Thermophysical and Mechanical Properties Prediction of Rear-earth High-entropy Pyrochlore Based on Deep-learning Potential |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2507.17032 |