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Molecular dynamics study of thermal conductivities of cubic diamond, lonsdaleite, and nanotwinned diamond via machine-learned potential |
| Jia-Hao Xiong(熊佳豪)1,4,5,†, Zi-Jun Qi(戚梓俊)1,4,†, Kang Liang(梁康)1,2,4, Xiang Sun(孙祥)1,2,4, Zhan-Peng Sun(孙展鹏)1,4, Qi-Jun Wang(汪启军)1,4, Li-Wei Chen(陈黎玮)3, Gai Wu(吴改)1,2,4,‡, and Wei Shen(沈威)1,2,4,§ |
1 The Institute of Technological Sciences, Wuhan University, Wuhan 430072, China;
2 School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China;
3 Department of Mechanical Engineering, The University of Tokyo, Tokyo, 113-8656, Japan;
4 Wuhan University Shenzhen Research Institute, Shenzhen 518057, China;
5 Hongyi Honor College, Wuhan University, Wuhan 430072, China |
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Abstract Diamond is a wide-bandgap semiconductor with a variety of crystal configurations, and has the potential applications in the field of high-frequency, radiation-hardened, and high-power devices. There are several important polytypes of diamonds, such as cubic diamond, lonsdaleite, and nanotwinned diamond (NTD). The thermal conductivities of semiconductors in high-power devices at different temperatures should be calculated. However, there has been no reports about thermal conductivities of cubic diamond and its polytypes both efficiently and accurately based on molecular dynamics (MD). Here, using interatomic potential of neural networks can provide obvious advantages. For example, comparing with the use of density functional theory (DFT), the calculation time is reduced, while maintaining high accuracy in predicting the thermal conductivities of the above-mentioned three diamond polytypes. Based on the neuroevolution potential (NEP), the thermal conductivities of cubic diamond, lonsdaleite, and NTD at 300 K are respectively 2507.3 W·m-1·K-1, 1557.2 W·m-1·K-1, and 985.6 W·m-1·K-1, which are higher than the calculation results based on Tersoff-1989 potential (1508 W·m-1·K-1, 1178 W·m-1·K-1, and 794 W·m-1·K-1, respectively). The thermal conductivities of cubic diamond and lonsdaleite, obtained by using the NEP, are closer to the experimental data or DFT data than those from Tersoff-potential. The molecular dynamics simulations are performed by using NEP to calculate the phonon dispersions, in order to explain the possible reasons for discrepancies among the cubic diamond, lonsdaleite, and NTD. In this work, we propose a scheme to predict the thermal conductivity of cubic diamond, lonsdaleite, and NTD precisely and efficiently, and explain the differences in thermal conductivity among cubic diamond, lonsdaleite, and NTD.
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Received: 05 May 2023
Revised: 25 June 2023
Accepted manuscript online: 06 July 2023
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PACS:
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81.05.ug
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(Diamond)
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02.70.Ns
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(Molecular dynamics and particle methods)
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65.40.-b
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(Thermal properties of crystalline solids)
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63.20.D-
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(Phonon states and bands, normal modes, and phonon dispersion)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos.62004141 and 52202045), the Fundamental Research Funds for the Central Universities, China (Grant Nos.2042022kf1028 and 2042023kf0112), the Knowledge Innovation Program of Wuhan-Shuguang, China (Grant Nos.2023010201020243 and 2023010201020255), the Natural Science Foundation of Hubei Province, China (Grant No.2022CFB606), and the Guangdong Basic and Applied Basic Research Fund: Guangdong--Shenzhen Joint Fund, China (Grant No.2020B1515120005). |
Corresponding Authors:
Gai Wu, Wei Shen
E-mail: wugai1988@whu.edu.cn;wei_shen_@whu.edu.cn
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Cite this article:
Jia-Hao Xiong(熊佳豪), Zi-Jun Qi(戚梓俊), Kang Liang(梁康), Xiang Sun(孙祥), Zhan-Peng Sun(孙展鹏), Qi-Jun Wang(汪启军), Li-Wei Chen(陈黎玮), Gai Wu(吴改), and Wei Shen(沈威) Molecular dynamics study of thermal conductivities of cubic diamond, lonsdaleite, and nanotwinned diamond via machine-learned potential 2023 Chin. Phys. B 32 128101
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[1] Chakraborty P, Xiong G P, Cao L, et al. 2018 Carbon 139 85
[2] Krauss A R, Auciello O, Gruen D M, et al. 2001 Diamond and Related Materials 10 1952
[3] Wort C J and Balmer R S 2008 Materials Today 11 22
[4] Willander M, Friesel M, Wahab Q U, et al. 2006 Journal of Materials Science: Materials in Electronics 17 1
[5] Huang Q, Yu D L, Xu B, et al. 2014 Nature 510 250
[6] Ma X L, Shi L P, He X D, et al. 2018 Carbon 133 69
[7] Lu K 2016 Nat. Rev. Mater. 1 16019
[8] Yue Y, Gao Y, Hu W, et al. 2020 Nature 582 370
[9] Xi Q, Zhong J, He J, et al. 2020 Chin. Phys. Lett. 37 104401
[10] Yang F, Zeng Q, Chen B, et al. 2022 Chin. Phys. Lett. 39 116301
[11] Pham C H, Lindsey R K, Fried L E, et al. 2022 J. Phys. Chem. Lett. 13 2934
[12] Obot I, Macdonald D and Gasem Z 2015 Corrosion Science 99 1
[13] Tersoff J 1989 Phys. Rev. B 39 5566
[14] Lindsay L and Broido D A 2010 Phys. Rev. B 81 205441
[15] Ma T, Chakraborty P, Guo X, et al. 2020 Int. J. Thermophys. 41 9
[16] Shi L P, Ma X L, Li M W, et al. 2021 Phys. Chem. Chem. Phys. 23 8336
[17] Shi L P, Ma X L, Zhong Y S, et al. 2021 Diamond and Related Materials 120 108618
[18] Hu S, An M, Yang N, et al. 2016 Nanotechnology 27 265702
[19] An M, Wang H, Yuan Y, et al. 2022 Surfaces and Interfaces 28 101690
[20] An M, Li L, Hu S, et al. 2020 Carbon 162 202
[21] Fan Z 2022 J. Phys.: Condens. Matter 34 125902
[22] Fan Z, Zeng Z, Zhang C, et al. 2021 Phys. Rev. B 104 104309
[23] Kresse G and Furthmüller J 1996 Phys. Rev. B 54 11169
[24] Perdew J P, Burke K and Ernzerhof M 1996 Phys. Rev. Lett. 77 3865
[25] Fan Z Y, Wang Y Z, Ying P H, et al. 2022 J. Chem. Phys. 157 114801
[26] Fan Z, Siro T and Harju A 2013 Comput. Phys. Commun. 184 1414
[27] Fan Z, Chen W, Vierimaa V, et al. 2017 Comput. Phys. Commun. 218 10
[28] Hoover W G, Evans D J, Hickman R B, et al. 1980 Phys. Rev. A 22 1690
[29] KuBo R 1957 J. Phys. Soc. Jpn. 12 570
[30] Che J, Çaǧin T, Deng W, et al. 2000 J. Chem. Phys. 113 6888
[31] Wang Z and Ruan X 2017 J. Appl. Phys. 121 044301
[32] Yanxon H, Zagaceta D, Tang B, et al. 2020 Machine Learning: Science and Technology 2 027001
[33] Ouyang Y, Yu C, He J, et al. 2022 Phys. Rev. B 105 115202
[34] Ouyang Y, Zhang Z, Yu C, et al. 2020 Chin. Phys. Lett. 37 126301
[35] McGaughey A J and Larkin J M 2014 Annu. Rev. Heat Transfer 17 49
[36] Chen Z, Zhang X and Pei Y 2018 Adv. Mater. 30 1705617
[37] Onn D G, Witek A, Qiu Y Z, et al. 1992 Phys. Rev. Lett. 68 2806
[38] Ward A, Broido D A, Stewart D A, et al. 2009 Phys. Rev. B 80 125203
[39] Friederich P, Häse F, Proppe J, et al. 2021 Nat. Mater. 20 750
[40] Anthony T R 1993 Philos. Trans. Roy. Soc. Lond. Ser. A: Math. Phys. Eng. Sci. 342 245
[41] Dong H, Xiao J, Melnik R, et al. 2016 Sci. Rep. 6 19575
[42] Holland M 1963 Phys. Rev. 132 2461
[43] Ren S Y and Dow J D 1982 Phys. Rev. B 25 3750
[44] Pavone P, Karch K, Schütt O, et al. 1993 Phys. Rev. B 48 3156 |
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