利用机器学习势函数揭示液态Mg–La合金的局部结构和热物理行为

赵佳; 冯泰熙; 路贵民

doi:10.1007/s12613-024-2928-2

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文章导航 > 矿物冶金与材料学报（英文） > 2024 > 二校

Jia Zhao, Taixi Feng, and Guimin Lu, Understanding the local structure and thermophysical behavior of Mg–La liquid alloys via machine learning potential, Int. J. Miner. Metall. Mater.,(2024). https://doi.org/10.1007/s12613-024-2928-2

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Jia Zhao, Taixi Feng, and Guimin Lu, Understanding the local structure and thermophysical behavior of Mg–La liquid alloys via machine learning potential, Int. J. Miner. Metall. Mater.,(2024). https://doi.org/10.1007/s12613-024-2928-2

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研究论文

利用机器学习势函数揭示液态Mg–La合金的局部结构和热物理行为

1)
华东理工大学国家盐湖资源综合利用工程技术研究中心，上海 200237，中国
2)
华东理工大学钾锂战略资源国际联合实验室，上海 200237，中国

通讯作者:
路贵民 E-mail: gmlu@ecust.edu.cn

文章亮点

(1) 为液态Mg–La合金体系开发了可靠的机器学习势函数。

(2) 系统地对液态Mg–La合金体系展开了研究。

(3) 全面地探究了温度和组成对液态Mg–La合金的局部结构影响。

(4) 构建了液态Mg–La合金的热物理性质数据库。

中文摘要

利用机器学习驱动的深度势能分子动力学（DPMD）模拟深入研究了液态Mg–La合金的局部结构和热物理行为，以促进Mg–La合金的发展。通过均方根误差（RMSE），能量和力数据以及结构信息的对比结果考察了训练的深度势函数（DP）模型的可靠性。分析了液态Mg–La合金局部结构的组成和温度依赖性。Mg–La合金体系中镁含量对体系中原子对的第一配位层影响与温度对其作用规律一致。结构因子中的预峰信号表明液态Mg–La合金中存在中程有序性，且当Mg含量为80at%时最明显。体系的中程有序性会随温度升高而消失。利用DPMD模拟预测了液态Mg–La合金的密度、自扩散系数和剪切粘度，并讨论了这些性质随体系中Mg含量和温度变化的规律，建立了相应的性质数据库。最后，计算了1200 K时液态Mg–La合金的混合焓和元素活度。

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Research Article

Understanding the local structure and thermophysical behavior of Mg–La liquid alloys via machine learning potential

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Abstract

Abstract

The local structure and thermophysical behavior of Mg–La liquid alloys were in-depth understood using deep potential molecular dynamic (DPMD) simulation driven via machine learning to promote the development of Mg–La alloys. The robustness of the trained deep potential (DP) model was thoroughly evaluated through several aspects, including root-mean-square errors (RMSEs), energy and force data, and structural information comparison results; the results indicate the carefully trained DP model is reliable. The component and temperature dependence of the local structure in the Mg–La liquid alloy was analyzed. The effect of Mg content in the system on the first coordination shell of the atomic pairs is the same as that of temperature. The pre-peak demonstrated in the structure factor indicates the presence of a medium-range ordered structure in the Mg–La liquid alloy, which is particularly pronounced in the 80at% Mg system and disappears at elevated temperatures. The density, self-diffusion coefficient, and shear viscosity for the Mg–La liquid alloy were predicted via DPMD simulation, the evolution patterns with Mg content and temperature were subsequently discussed, and a database was established accordingly. Finally, the mixing enthalpy and elemental activity of the Mg–La liquid alloy at 1200 K were reliably evaluated, which provides new guidance for related studies.
- magnesium–lanthanum liquid alloys;
- local structure;
- macroscopic properties;
- thermodynamic behavior;
- deep potential molecular dynamic simulation

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[1]	T. Takenaka, T. Ono, Y. Narazaki, Y. Naka, and M. Kawakami, Improvement of corrosion resistance of magnesium metal by rare earth elements, Electrochim. Acta, 53(2007), No. 1, p. 117. doi: 10.1016/j.electacta.2007.03.027
[2]	L.B. Tong, Q.X. Zhang, Z.H. Jiang, et al., Microstructures, mechanical properties and corrosion resistances of extruded Mg–Zn–Ca–xCe/La alloys, J. Mech. Behav. Biomed. Mater., 62(2016), p. 57. doi: 10.1016/j.jmbbm.2016.04.038
[3]	Q.X. Zhang, L.B. Tong, L.R. Cheng, Z.H. Jiang, J. Meng, and H.J. Zhang, Effect of Ce/La microalloying on microstructural evolution of Mg–Zn–Ca alloy during solution treatment, J. Rare Earths, 33(2015), No. 1, p. 70. doi: 10.1016/S1002-0721(14)60385-9
[4]	J. Rong, J.N. Zhu, W.L. Xiao, X.Q. Zhao, and C.L. Ma, A high pressure die cast magnesium alloy with superior thermal conductivity and high strength, Intermetallics, 139(2021), art. No. 107350. doi: 10.1016/j.intermet.2021.107350
[5]	A. Gökçe, Metallurgical assessment of novel Mg–Sn–La alloys produced by high-pressure die casting, Met. Mater. Int., 26(2020), No. 7, p. 1036. doi: 10.1007/s12540-019-00539-1
[6]	Y.C. Tsai, C.Y. Chou, S.L. Lee, C.K. Lin, J.C. Lin, and S.W. Lim, Effect of trace La addition on the microstructures and mechanical properties of A356 (Al–7Si–0.35Mg) aluminum alloys, J. Alloys Compd., 487(2009), No. 1-2, p. 157. doi: 10.1016/j.jallcom.2009.07.183
[7]	C.P. Guo and Z.M. Du, Thermodynamic assessment of the La–Mg system, J. Alloys Compd., 385(2004), No. 1-2, p. 109. doi: 10.1016/j.jallcom.2004.04.105
[8]	M.Y. Li, S.Z. Du, R.X. Liu, S.J. Lu, P. Jia, and H.R. Geng, Local structure and its change of Al–Ni alloy melts, J. Mol. Liq., 200(2014), p. 168. doi: 10.1016/j.molliq.2014.10.007
[9]	P. Srirangam, M.J. Kramer, and S. Shankar, Effect of strontium on liquid structure of Al–Si hypoeutectic alloys using high-energy X-ray diffraction, Acta Mater., 59(2011), No. 2, p. 503. doi: 10.1016/j.actamat.2010.09.050
[10]	C. Notthoff, B. Feuerbacher, H. Franz, D.M. Herlach, and D. Holland-Moritz, Direct determination of metastable phase diagram by synchrotron radiation experiments on undercooled metallic melts, Phys. Rev. Lett., 86(2001), No. 6, p. 1038. doi: 10.1103/PhysRevLett.86.1038
[11]	Y.B. Wang, S.S. Jia, M.G. Wei, L.M. Peng, Y.J. Wu, and X.T. Liu, Research progress on solidification structure of alloys by synchrotron X-ray radiography: A review, J. Magnes. Alloys, 8(2020), No. 2, p. 396. doi: 10.1016/j.jma.2019.08.003
[12]	L.F. Zhang, H. Wang, R. Car, and Weinan E, Phase diagram of a deep potential water model, Phys. Rev. Lett., 126(2021), No. 23, art. No. 236001. doi: 10.1103/PhysRevLett.126.236001
[13]	J.Z. Zeng, L.Q. Cao, M.Y Xu, T. Zhu, and J.Z.H. Zhang, Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation, Nat. Commun., 11(2020), No. 1, art. No. 5713. doi: 10.1038/s41467-020-19497-z
[14]	M.Y. Yang, U. Raucci, and M. Parrinello, Reactant-induced dynamics of lithium imide surfaces during the ammonia decomposition process, Nat. Catal., 6(2023), No. 9, p. 829. doi: 10.1038/s41929-023-01006-2
[15]	J.C. Liu, L.L. Luo, H. Xiao, J.F. Zhu, Y. He, and J. Li, Metal affinity of support dictates sintering of gold catalysts, J. Am. Chem. Soc., 144(2022), No. 45, p. 20601. doi: 10.1021/jacs.2c06785
[16]	J.Y. Jiao, G.M. Lai, L. Zhao, et al., Self-healing mechanism of lithium in lithium metal, Adv. Sci., 9(2022), No. 12, art. No. 2105574. doi: 10.1002/advs.202105574
[17]	J. Zhao, T.X. Feng, G.M. Lu, and J.G. Yu, Insights into the local structure evolution and thermophysical properties of NaCl–KCl–MgCl₂–LaCl₃ melt driven by machine learning, J. Mater. Chem. A, 11(2023), No. 44, p. 23999. doi: 10.1039/D3TA03434H
[18]	T.R. Xu, X.J. Li, Y. Wang, and Z.F. Tang, Development of deep potentials of molten MgCl₂–NaCl and MgCl₂–KCl salts driven by machine learning, ACS Appl. Mater. Interfaces, 15(2023), No. 11, p. 14184.
[19]	C.S. Zhu, W.J. Dong, Z.H. Gao, L.J. Wang, and G.Z. Li, Deep Potential fitting and mechanical properties study of MgAlSi alloy, Comput. Mater. Sci., 239(2024), art. No. 112966. doi: 10.1016/j.commatsci.2024.112966
[20]	N. Xu, Y. Shi, Y. He, and Q. Shao, A deep-learning potential for crystalline and amorphous Li–Si alloys, J. Phys. Chem. C, 124(2020), No. 30, p. 16278. doi: 10.1021/acs.jpcc.0c03333
[21]	Q. Wang, B. Zhai, H.P. Wang, and B. Wei, Atomic structure of liquid refractory Nb₅Si₃ intermetallic compound alloy based upon deep neural network potential, J. Appl. Phys., 130(2021), No. 18, art. No. 185103. doi: 10.1063/5.0067157
[22]	T.Q. Wen, C.Z. Wang, M.J. Kramer, et al., Development of a deep machine learning interatomic potential for metalloid-containing Pd–Si compounds, Phys. Rev. B, 100(2019), No. 17, art. No. 174101. doi: 10.1103/PhysRevB.100.174101
[23]	B. Zhai and H.P. Wang, Accurate interatomic potential for the nucleation in liquid Ti–Al binary alloy developed by deep neural network learning method, Comput. Mater. Sci., 216(2023), art. No. 111843. doi: 10.1016/j.commatsci.2022.111843
[24]	R.E. Ryltsev and N.M. Chtchelkatchev, Deep machine learning potentials for multicomponent metallic melts: Development, predictability and compositional transferability, J. Mol. Liq., 349(2022), art. No. 118181. doi: 10.1016/j.molliq.2021.118181
[25]	L. Tang, Z.J. Yang, T.Q. Wen, K.M. Ho, M.J. Kramer, and C.Z. Wang, Development of interatomic potential for Al–Tb alloys using a deep neural network learning method, Phys. Chem. Chem. Phys., 22(2020), No. 33, p. 18467. doi: 10.1039/D0CP01689F
[26]	L. Tang, K.M. Ho, and C.Z. Wang, Molecular dynamics simulation of metallic Al-Ce liquids using a neural network machine learning interatomic potential, J. Chem. Phys., 155(2021), No. 19, art. No. 194503. doi: 10.1063/5.0066061
[27]	X. He, J.D. Liu, C. Yang, and G. Jiang, Predicting thermodynamic stability of magnesium alloys in machine learning, Comput. Mater. Sci., 223(2023), art. No. 112111. doi: 10.1016/j.commatsci.2023.112111
[28]	Y.N. Wang, X.Y. Wang, W.R. Jiang, H. Wang, and F.Z. Dai, Domain structures and stacking sequences of Mg–Zn–Y long-period stacking ordered (LPSO) structures predicted by deep-learning potential, Mater. Today Commun., 38(2024), art. No. 108301. doi: 10.1016/j.mtcomm.2024.108301
[29]	W.R. Jiang, Y.Z. Zhang, L.F. Zhang, and H. Wang, Accurate deep potential model for the Al–Cu–Mg alloy in the full concentration space, Chin. Phys. B, 30(2021), No. 5, art. No. 050706. doi: 10.1088/1674-1056/abf134
[30]	H.D. Wang, Y.Z. Zhang, L.F. Zhang, and H. Wang, Crystal structure prediction of binary alloys via deep potential, Front. Chem., 8(2020), art. No. 589795. doi: 10.3389/fchem.2020.589795
[31]	C.H. Li, H.L. Zhang, D.L. Guo, and W. Zeng, Crystal structure prediction and property calculation of Al₂CuMg by deep learning potential, J. Mater. Eng. Perform., (2023). https://doi.org/10.1007/s11665-023-08944-9
[32]	G. Kresse and J. Furthmüller, Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set, Phys. Rev. B, 54(1996), No. 16, p. 11169. doi: 10.1103/PhysRevB.54.11169
[33]	G. Kresse and D. Joubert, From ultrasoft pseudopotentials to the projector augmented-wave method, Phys. Rev. B, 59(1999), No. 3, p. 1758. doi: 10.1103/PhysRevB.59.1758
[34]	J.P. Perdew, K. Burke, and M. Ernzerhof, Generalized gradient approximation made simple, Phys. Rev. Lett., 77(1996), No. 18, p. 3865. doi: 10.1103/PhysRevLett.77.3865
[35]	S. Grimme, J. Antony, S. Ehrlich, and H. Krieg, A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H–Pu, J. Chem. Phys., 132(2010), No. 15, art. No. 154104. doi: 10.1063/1.3382344
[36]	Y.Z. Zhang, H.D. Wang, W.J. Chen, et al., DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models, Comput. Phys. Commun., 253(2020), art. No. 107206. doi: 10.1016/j.cpc.2020.107206
[37]	L.F. Zhang, J.Q. Han, H. Wang, R. Car, and Weinan E, Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics, Phys. Rev. Lett., 120(2018), No. 14, art. No. 143001. doi: 10.1103/PhysRevLett.120.143001
[38]	L. Zhang, J. Han, H. Wang, W.A. Saidi, R. Car, and Weinan E, End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems, [in] Proceedings of the 32nd Conference on Neural Information Processing Systems, Montréal, 2018, p. 4441.
[39]	S. Plimpton, Fast parallel algorithms for short-range molecular dynamics, J. Comput. Phys., 117(1995), No. 1, p. 1. doi: 10.1006/jcph.1995.1039
[40]	S.L. Roux and P. Jund, Ring statistics analysis of topological networks: New approach and application to amorphous GeS₂ and SiO₂ systems, Comput. Mater. Sci., 49(2010), No. 1, p. 70. doi: 10.1016/j.commatsci.2010.04.023
[41]	S. Dalgic, S. Dalgic, S. Sengul, M. Celtek, and G. Tezgor, Liquid structure of some rare-earth metals using an analytic pair potential, J. Optoelectron. Adv. Mater., 3(2001), No. 4, p. 831.
[42]	J.F. Wax, R. Albaki, and J.L. Bretonnet, Structural and dynamical properties of liquid alkaline-earth metals near the melting point, Phys. Rev. B, 62(2000), No. 22, p. 14818. doi: 10.1103/PhysRevB.62.14818
[43]	J. Wang, Z. Sun, G. Lu, and J. Yu, Molecular dynamics simulations of the local structures and transport coefficients of molten alkali chlorides, J. Phys. Chem. B, 118(2014), No. 34, p. 10196. doi: 10.1021/jp5050332
[44]	T.E. Faber and J.M. Ziman, A theory of the electrical properties of liquid metals, Philos. Mag., 11(1965), No. 109, p. 153. doi: 10.1080/14786436508211931
[45]	H.T. Reijers, W. van der Lugt, and M.L. Saboungi, Molecular-dynamics study of liquid NaPb, KPb, RbPb, and CsPb alloys, Phys. Rev. B., 42(1990), No. 6, p. 3395. doi: 10.1103/PhysRevB.42.3395
[46]	S. Takeda, S. Harada, S. Tamaki, E. Matsubara, and Y. Waseda, Structural study of liquid Na–Pb alloys by neutron diffraction, J. Phys. Soc. Jpn., 56(1987), No. 11, p. 3934. doi: 10.1143/JPSJ.56.3934
[47]	V.I. Kohonenko, A.L. Sukhman, S.L. Gruverman, and V.V. Torokin, Density and surface tension of liquid rare earth metals, scandium, and yttrium, Phys. Status Solidi A, 84(1984), No. 2, p. 423. doi: 10.1002/pssa.2210840210
[48]	P.J. McGonigal, A.D. Kirshenbaum, and A.V. Grosse, The liquid temperature range, density, and critical constants of magnesium, J. Phys. Chem., 66(1962), No. 4, p. 737. doi: 10.1021/j100810a038
[49]	S.D. Korkmaz and Ş. Korkmaz, Atomic transport properties of liquid alkaline earth metals: A comparison of scaling laws proposed for diffusion and viscosity, Modelling Simul. Mater. Sci. Eng., 15(2007), No. 3, p. 285. doi: 10.1088/0965-0393/15/3/007
[50]	R. Vuilleumier, A. Seitsonen, N. Sator, and B. Guillot, Structure, equation of state and transport properties of molten calcium carbonate (CaCO₃) by atomistic simulations, Geochim. Cosmochim. Acta, 141(2014), p. 547. doi: 10.1016/j.gca.2014.06.037
[51]	X.J. Li, J. Song, S.P. Shi, et al., Dynamic fluctuation of U³⁺ coordination structure in the molten LiCl–KCl eutectic via first principles molecular dynamics simulations, J. Phys. Chem. A, 121(2017), No. 3, p. 571. doi: 10.1021/acs.jpca.6b10193
[52]	H.P. Patel, Y.A. Sonvane, P.B. Thakor, and A.V. Prajapati, Shear viscosity coefficient of liquid lanthanides, AIP Conf. Proc., 1661(2015), No. 1, art. No. 110012.
[53]	I. Yokoyama and S. Tsuchiya, Excess entropy, diffusion coefficient, viscosity coefficient and surface tension of liquid simple metals from diffraction data, Mater. Trans., 43(2002), No. 1, p. 67. doi: 10.2320/matertrans.43.67
[54]	T.T. Xu, J.Y. Li, R.L. Xiao, J.Y. Qin, Y. Ruan, and H. Li, The mixing enthalpy and liquid structural properties of Ti–Al alloys by ab inito molecular dynamics simulation, J. Phase Equilib. Diffus., 43(2022), No. 5, p. 585. doi: 10.1007/s11669-022-01015-x
[55]	A. Berche, P. Benigni, J. Rogez, and M.C. Record, Thermodynamic assessment of the La–Mg system, Calphad, 35(2011), No. 4, p. 580. doi: 10.1016/j.calphad.2011.10.001
[56]	R. Agarwal, H. Feufel, and F. Sommer, Calorimetric measurements of liquid La–Mg, Mg–Yb and Mg–Y alloys, J. Alloys Compd., 217(1995), No. 1, p. 59. doi: 10.1016/0925-8388(94)01290-X