-
Metallic materials are widely used in industrial applications due to their excellent electrical transport properties and superior thermal dissipation performance. However, experimental measurements of electrical and thermal conductivity under high-temperature and high-pressure conditions are challenging and costly. This makes numerical simulation an efficient alternative. In this study, we developed a computational software named TRansport at EXtremes (TREX). It is based on the Kubo-Greenwood (KG) formula combined with first-principles molecular dynamics. This software is used to calculate electrical conductivity and electronic thermal conductivity. Using magnesium and magnesium-aluminum alloy AZ31B as research subjects, we systematically investigated their electrical and thermal transport properties. The temperature range was 300~1200 K, and the pressure range was 0~50 GPa. The methodology involved employing first-principles molecular dynamics simulations to obtain equilibrium configurations of high-temperature disordered structures. Electrical conductivity and electronic thermal conductivity were calculated using the KG formula. Lattice thermal conductivity was determined via the Slack equation. To validate the reliability of our approach, we performed comparative calculations using the Boltzmann transport equation (BTE). The results were cross-verified with experimental data from Sichuan University and the Aerospace Materials Testing and Analysis Center. The findings demonstrate that the maximum relative error between computational and experimental results is within 20%. This confirms the accuracy of our method. Additionally, we elucidated the variation patterns of electrical and thermal conductivity in magnesium and AZ31B alloy with temperature and pressure. These patterns include the reduction in electrical conductivity due to aluminum doping, the significant enhancement of conductivity under high pressure, and the unique temperature-induced thermal conductivity enhancement in AZ31B alloy. The TREX program developed in this study and the established performance dataset provide essential tools and data support. They are useful for research on electrical and thermal transport mechanisms in metallic materials under extreme conditions, as well as for engineering applications. The dataset supporting this article can be accessed and obtained from the Science Data Bank repository at
https://www.doi.org/10.57760/sciencedb.j00213.00128 . (During the review stage, please access the dataset via the private access link:https://www.scidb.cn/s/jA7rq2 .)
-
Keywords:
- Kubo-Greenwood /
- Magnesium and AZ31B alloy /
- Electrical conductivity /
- Thermal conductivity
-
图 2 TREX程序(基于KG公式与AIMD模拟) 计算电导率和电子热导率的流程示意图. 红色虚线框表示TREX程序的核心功能(包括平衡构型提取、电子输运性质计算等); 蓝色框表示与第一性原理计算软件相关的计算内容(如第一性原理分子动力学、电子结构、跃迁矩阵等)
Figure 2. Schematic diagram of the workflow for calculating electrical conductivity and electronic thermal conductivity using the TREX code (based on the Kubo-Greenwood formula and AIMD simulations). The red dashed box indicates the core functions of the TREX code (including equilibrium configuration extraction, electronic transport property calculations, etc.). The blue boxes represent calculations related to first-principles software (such as ab initio molecular dynamics, electronic structure, and transition matrices).
图 1 512个原子的AZ31B合金超胞结构示意图. 蓝色原子表示镁, 红色原子表示铝
Figure 1. Schematic diagram of the supercell structure of AZ31B alloy with 512 atoms. Blue atoms represent magnesium, and red atoms represent aluminum.
图 3 图(a) 表示镁单质电导率计算结果与实验值对比图, 竖点线表示镁在常压条件下的熔化温度. 图(b) 表示AZ31B合金电导率计算结果与实验值对比图, 黑色(红色、蓝色) 图例表示0 GPa (40、50 GPa) 的实验和计算结果
Figure 3. Figure (a) shows the comparison between the calculated electrical conductivity of magnesium single crystal and the experimental values, with the vertical dotted line indicating the melting temperature of magnesium under ambient pressure. Figure (b) presents the comparison between the calculated electrical conductivity of AZ31B alloy and the experimental values, where the black (red, blue) legend represents the experimental and calculated results at 0 GPa (40, 50 GPa).
图 4 图(a) 呈现了镁的热导率各分项贡献的组成. 图(b) 呈现了AZ31B合金的热导率各分项贡献的组成, 实线和实心(虚线和空心) 图例表示0 GPa (40 GPa) 的计算结果. ETC表示电子热导率, LTC表示晶格热导率, TTC表示总热导率
Figure 4. Figure (a) shows the composition of various contributions to the thermal conductivity of magnesium. Figure (b) presents the composition of different contributions to the thermal conductivity of AZ31B alloy, where solid lines and solid symbols (dashed lines and hollow symbols) represent the calculated results at 0 GPa (40 GPa). Here, ETC denotes the electronic thermal conductivity, LTC represents the lattice thermal conductivity, and TTC stands for the total thermal conductivity.
图 5 图(a) 表示镁单质热导率计算结果与实验值对比图, 竖点线表示镁在常压条件下的熔化温度. 图(b) 表示AZ31B合金热导率计算结果与实验值对比图, 黑色(红色) 图例表示0 GPa (40 GPa) 的实验和计算结果, 红色虚线表示对40 GPa实验结果的线性拟合
Figure 5. Figure (a) presents a comparison between the calculated and experimental values of thermal conductivity for pure magnesium, with the vertical dotted line indicating the melting temperature of magnesium under ambient pressure. Figure (b) shows a comparison between the calculated and experimental values of thermal conductivity for AZ31B alloy, where black (red) symbols represent the experimental and computational results at 0 GPa (40 GPa), and the red dashed line denotes the linear fit to the experimental data at 40 GPa.
表 1 电子弛豫时间τ (单位: 10–14 s) 随温度T变化的拟合公式$ \tau=AT^{-r} $
Table 1. The fitting formula for the electron relaxation time τ (unit: 10–14 s) as a function of temperature T is given by $ \tau = A T^{-r} $.
材料 压强(GPa) 参数A 参数r R2 Mg 0 1306.36 1.12 0.9999 AZ31B 0 52.54 0.58 0.9976 AZ31B 40 230.74 0.76 0.9953 AZ31B 50 182.05 0.73 0.9993 
DownLoad: CSV
-
[1] 王奥, 盛宇飞, 鲍华 2024 73 037201
Google Scholar
Wang Ao S Y F, Hua B 2024 Acta Phys. Sin. 73 037201
Google Scholar
[2] Ventura G, Perfetti M 2014 Electrical and Thermal Conductivity (Dordrecht: Springer Netherlands), pp 131–168
[3] Burger N, Laachachi A, Ferriol M, Lutz M, Toniazzo V, Ruch D 2016 Progress in Polymer Science 61 1
Google Scholar
[4] Reif-Acherman S 2011 Revista Brasileira de Ensino de Física 33 4602
[5] Demyanov G S, Knyazev D V, Levashov P R 2022 Phys. Rev. E 105 035307
[6] Vlček V, De Koker N, Steinle-Neumann G 2012 Phys. Rev. B 85 184201
Google Scholar
[7] Naumov I I, Hemley R J 2015 Phys. Rev. Lett. 114 156403
Google Scholar
[8] Matsuoka T, Shimizu K 2009 Nature 458 186
Google Scholar
[9] Kietzmann A, Redmer R, Desjarlais M P, Mattsson T R 2008 Phys. Rev. Lett. 101 070401
Google Scholar
[10] 崔洋, 李寿航, 应韬, 鲍华, 曾小勤 2021 金属学报 57 375
Yang C, Shouhang L, Tao Y, Hua B, Xiaoqin Z 2021 Acta Metallurgica Sinica 57 375
[11] Reif F 2009 Fundamentals of statistical and thermal physics (New York: McGraw-Hill), pp 483–522
[12] Bulusu A, Walker D 2008 Superlattices and Microstructures 44 1
Google Scholar
[13] Migdal K, Zhakhovsky V, Yanilkin A, Petrov Y V, Inogamov N 2019 Applied Surface Science 478 818
Google Scholar
[14] Calderín L, Karasiev V, Trickey S 2017 Computer Physics Communications 221 118
Google Scholar
[15] French M, Mattsson T R 2014 Phys. Rev. B 90 165113
Google Scholar
[16] Kramer D A 2010 Springer Handbook of Materials Data (Cham: Springer International Publishing), pp 151–159
[17] Zhang Q, Fu J, Zhou J, Qi L, Li H 2024 Chemical Engineering Journal 496 154023
Google Scholar
[18] Xin W, Wang S, Yan Z 2025 Materials Today Communications 43 111584
Google Scholar
[19] Madsen G K, Carrete J, Verstraete M J 2018 Computer Physics Communications 231 140
Google Scholar
[20] Slack G A 1973 Journal of Physics and Chemistry of Solids 34 321
Google Scholar
[21] Moseley L, Lukes T 1978 American Journal of Physics 46 676
Google Scholar
[22] Knyazev D V, Levashov P R 2019 Contributions to Plasma Physics 59 345
Google Scholar
[23] Hohenberg P, Kohn W 1964 Phys. Rev. 136 B864
Google Scholar
[24] Kohn W, Sham L J 1965 Physical Review 140 A1133
Google Scholar
[25] Kresse G, Furthmüller J 1996 Computational Materials Science 6 15
Google Scholar
[26] Kresse G, Furthmüller J 1996 Phys. Rev. B 54 11169
Google Scholar
[27] Blöchl P 1994 Phys. Rev. B 50 17953
Google Scholar
[28] Perdew J P, Burke K, Ernzerhof M 1996 Phys. Rev. Lett. 77 3865
Google Scholar
[29] Kubo R, Yokota M, Nakajima S 1957 Journal of the Physical Society of Japan 12 1203
Google Scholar
[30] Tian F, Lin D Y, Gao X, Zhao Y F, Song H F 2020 Journal of Chemical Physics 153 034101
Google Scholar
[31] Desjarlais M, Kress J, Collins L 2002 Phys. Rev. E 66 025401
[32] Choi G, Kim H S, Lee K, Park S H, Cha J, Chung I, Lee W B 2017 Journal of Alloys and Compounds 727 1237
Google Scholar
[33] Ahmad S, Mahanti S D 2010 Phys. Rev. B 81 165203
Google Scholar
[34] Lang H N D, van Kempen H, Wyder P 1978 Journal of Physics F: Metal Physics 8 L39
Google Scholar
[35] Iida T, Guthrie R I L 1988 The physical properties of liquid metals (New York: Oxford Clarendon Press), pp 266–253
[36] Pan H, Pan F, Wang X, Peng J, Gou J, She J, Tang A 2013 International Journal of Thermophysics 34 1336
Google Scholar
[37] Chen F, Huang Q, Jiang Z, Zhao J, Sun B, Li Y 2015 Vacuum 115 80
Google Scholar
[38] Motta C, El-Mellouhi F, Sanvito S 2015 Scientific reports 5 12746
Google Scholar
[39] Madsen G K, Singh D J 2006 Computer Physics Communications 175 67
Google Scholar
[40] Yue S Y, Zhang X, Stackhouse S, Qin G, Di Napoli E, Hu M 2016 Phys. Rev. B 94 075149
Google Scholar
[41] Blakemore J S 1985 Solid state physics (Cambridge: Cambridge University Press), pp 149–292
[42] Ho C Y, Powell R W, Liley P E 1972 Journal of Physical and Chemical Reference Data 1 279
Google Scholar
[43] Ying T, Zheng M, Li Z, Qiao X 2014 Journal of Alloys and Compounds 608 19
Google Scholar
DownLoad:
Metrics
- Abstract views: 45
- PDF Downloads: 1
- Cited By: 0
