Machine learning potential for predicting thermal conductivity of θ-phase and amorphous Tantalum Nitride
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915428404559872 |
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| author | Zong, Zhicheng Qin, Yangjun Zhan, Jiahong Fang, Haisheng Yang, Nuo |
| author_facet | Zong, Zhicheng Qin, Yangjun Zhan, Jiahong Fang, Haisheng Yang, Nuo |
| contents | Tantalum nitride (TaN) has attracted considerable attention due to its unique electronic and thermal properties, high thermal conductivity, and applications in electronic components. However, for the θ-phase of TaN, significant discrepancies exist between previous experimental measurements and theoretical predictions. In this study, deep potential models for TaN in both the θ-phase and amorphous phase were developed and employed in molecular dynamics simulations to investigate the thermal conductivities of bulk and nanofilms. The simulation results were compared with reported experimental and theoretical results, and the mechanism for differences were discussed. This study provides insights into the thermal transport mechanisms of TaN, offering guidance for its application in advanced electronic and thermal management devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03297 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine learning potential for predicting thermal conductivity of θ-phase and amorphous Tantalum Nitride Zong, Zhicheng Qin, Yangjun Zhan, Jiahong Fang, Haisheng Yang, Nuo Materials Science Computational Physics Tantalum nitride (TaN) has attracted considerable attention due to its unique electronic and thermal properties, high thermal conductivity, and applications in electronic components. However, for the θ-phase of TaN, significant discrepancies exist between previous experimental measurements and theoretical predictions. In this study, deep potential models for TaN in both the θ-phase and amorphous phase were developed and employed in molecular dynamics simulations to investigate the thermal conductivities of bulk and nanofilms. The simulation results were compared with reported experimental and theoretical results, and the mechanism for differences were discussed. This study provides insights into the thermal transport mechanisms of TaN, offering guidance for its application in advanced electronic and thermal management devices. |
| title | Machine learning potential for predicting thermal conductivity of θ-phase and amorphous Tantalum Nitride |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2508.03297 |