Crystal Structure Prediction and Property Calculation of Al2CuMg by Deep Learning Potential
Source abstract
Abstract
Al2CuMg phases are important precipitates formed by Al-Cu-Mg alloys after heat treatment and can significantly improve the mechanical properties of the alloy. In recent years, crystal structure prediction and property calculation have received great attention. However, there is a lack of theoretical predictions and simulations for Al2CuMg phases. In this study, the structure of Al2CuMg is predicted using deep learning potential, and six candidate phases are screened. The mechanical and thermal properties of the six phases (samples 1-6) are calculated using the density functional theory. The screened sample phases feature negative cohesive energies and display no imaginary frequencies in the phonon dispersions. Sample 3 features the highest compressibility and plasticity. Sample 4 exhibits the largest rigidity and hardness. In the a-direction, sample 4 displays the highest tensile strength, reaching 15.60 GPa and a corresponding strain of 16.10%. Additionally, sample 4 also exhibits the largest Debye temperature. Sample 1 features the highest thermal conductivity in the temperature range of 30-800 K.
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Research context
OpenAlex topics: Aluminum Alloys Composites Properties · Aluminum Alloy Microstructure Properties · Metal and Thin Film Mechanics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (49)
Show source-provided references
1. X.L. Chen, C.D. Marioara, S.J. Andersen, J. Friis, A. Lervik, R. Holmestad, and E. Kobayashi, Precipitation Processes and Structural Evolutions of Various GPB Zones and Two Types of S Phases in a Cold-Rolled Al-Mg-Cu Alloy, Mater. Des., 2021, 199, p 109425.10.1016/j.matdes.2020.109425 ↗
2. M.J. Styles, R.K.W. Marchau, T.J. Bastow, H.E.A. Brand, M.A. Gibson, and C.R. Hutchinson, The Competition Between Metastable and Equilibrium S (Al2CuMg) Phase during the Decomposition of Al-Cu-Mg Alloys, Acta Mater., 2015, 95, p 64–80.10.1016/j.actamat.2015.07.011 ↗
3. S.C. Wang and M.J. Starink, Precipitates and Intermetallic Phases in Precipitation Hardening Al-Cu-Mg-(Li) Based Alloys, Int. Mater. Rev., 2005, 50, p 193–215.10.1179/174328005X14357 ↗
4. V.A. Esin, L. Briez, M. Sennour, A. Köster, E. Gratiot, and J. Crépin, Precipitation-Hardness Map for Al-Cu-Mg Alloy (AA2024-T3), J. Alloys Compd., 2021, 854, p 157164.10.1016/j.jallcom.2020.157164 ↗
5. Z.G. Chen, J.K. Ren, Z.G. Yuan, and S.P. Ringer, Enhanced Strength-Plasticity Combination in an Al-Cu-Mg Alloy-Atomic Scale Microstructure Regulation and Strengthening Mechanisms, Mater. Sci. Eng. A, 2020, 787, p 139447.10.1016/j.msea.2020.139447 ↗
6. G.B. Winkelman, K. Raviprasad, and B.C. Muddle, Orientation Relationships and Lattice Matching for the S Phase in Al-Cu-Mg Alloys, Acta Mater., 2007, 55, p 3213–3228.10.1016/j.actamat.2007.01.011 ↗
7. F.Z. Dai, B. Wen, H. Xiang, and Y.C. Zhou, Grain Boundary Strengthening in ZrB2 by Segregation of W: Atomistic Simulations with Deep Learning Potential, J. Eur. Ceram. Soc., 2020, 40, p 5029–5036.10.1016/j.jeurceramsoc.2020.06.007 ↗
8. H. Wang, L.F. Zhang, J.Q. Han, and E. Weinan, DeePMD-kit: A Deep Learning Package for Many-Body Potential Energy Representation and Molecular Dynamics, Comput. Phys. Commun., 2018, 228, p 178–184.10.1016/j.cpc.2018.03.016 ↗
9. L.F. Zhang, J.Q. Han, H. Wang, R. Car, and E. Weinai, Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics, Phys. Rev. Lett., 2018, 120, p 143001.10.1103/PhysRevLett.120.143001 ↗
10. F.Z. Dai, B. Wen, Y.J. Sun, H.M. Xiang, and Y.C. Zhou, Theoretical Prediction on Thermal and Mechanical Properties of High Entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2) C by Deep Learning Potential, J. Mater. Sci. Technol., 2020, 43, p 168–174.10.1016/j.jmst.2020.01.005 ↗
11. F.Z. Dai, Y.J. Sun, B. Wen, H.M. Xiang, and Y.C. Zhou, Temperature Dependent Thermal and Elastic Properties of High Entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2: Molecular Dynamics Simulation by Deep Learning Potential, J. Mater. Sci. Technol., 2021, 72, p 8–15.10.1016/j.jmst.2020.07.014 ↗
12. D.L. Guo, C.H. Li, K.J. Li, B. Shao, D.M. Chen, Y.L. Ma, J.C. Sun, X.L. Cao, W. Zeng, and X.P. Chang, The Thermoelectric Performance of New Structure SnSe Studied by Quotient Graph and Deep Learning Potential, Mater. Today Energy, 2021, 20, p 100665.10.1016/j.mtener.2021.100665 ↗
13. T.X. Feng, J. Zhao, W.S. Liang, and G.M. Lu, Molecular Dynamics Simulations of Lanthanum Chloride by Deep Learning Potential, Comput. Mater. Sci., 2021, 24, p 111014.
14. R.E. Ryltsev and N.M. Chtchelkatchev, Deep Machine Learning Potentials for Multicomponent Metallic Melts: Development, Predictability and Compositional Transferability, J. Mol. Liq., 2021, 27, p 118181.
15. T. Yamashita, S. Kanehira, N. Sato, H. Kino, K. Terayama, H. Sawahata, T. Sato, F. Utsuno, K. Tsuda, T. Miyake, and T. Oguchi, CrySPY: A Crystal Structure Prediction Tool Accelerated by Machine Learning, Sci. Technol. Adv. Mater. Methods, 2021, 1, p 87–97.
16. W.R. Jiang, Y.Z. Wang, L.F. Zhang, and H. Wang, Accurate Deep Potential Model for the Al-Cu-Mg alloy in the Full Concentration Space, Chin. Phys. B, 2021, 5, p 050706.10.1088/1674-1056/abf134 ↗
17. H.D. Wang, Y.Z. Zhang, L.F. Zhang, and H. Wang, Crystal Structure Prediction of Binary Alloy via Deep Potential, Front. Chem., 2020, 8, p 589795.10.3389/fchem.2020.589795 ↗
18. F. Mouhat and F.X. Coudert, Necessary and Sufficient Elastic Stability Conditions in Various Crystal Systems, Phys. Rev. B, 2014, 90, p 224104.10.1103/PhysRevB.90.224104 ↗
19. Z.J. Wu, E.J. Zhao, H.P. Xiang, X.F. Hao, X.J. Liu, and J. Meng, Crystal Structures and Elastic Properties of Superhard IrN2 and IrN3 from First Principles, Phys. Rev. B, 2007, 76, p 054115.10.1103/PhysRevB.76.054115 ↗
20. G.G. Kresse and J.J. Furthmüller, Efficiency of Ab-Initio Total Energy Calculations for Metals and Semiconductors Using a Plane-Wave Basis Set, Comput. Mater. Sci., 1996, 6, p 15–30.10.1016/0927-0256(96)00008-0 ↗
21. G.G. Kresse and J.J. Furthmüller, Efficient Iterative Schemes Forab Initiototal-Energy Calculations Using a Plane-Wave Basis Set, Phys. Rev. B Condens. Matter, 1996, 54, p 11169–11186.10.1103/PhysRevB.54.11169 ↗
22. H.J. Monkhorst and J.D. Pack, Special Points for Brillouin-Zone Integrations, Phys. Rev. B, 1976, 13, p 5188–5192.10.1103/PhysRevB.13.5188 ↗
23. T.T. Jia, Z.Z. Feng, S.P. Guo, X.M. Zhang, and Y.S. Zhang, Screening Promising Thermoelectric Materials in Binary Chalcogenides through High-Throughput Computations, ACS Appl. Mater. Interfaces, 2020, 12, p 11852–11864.10.1021/acsami.9b23297 ↗
24. B.R. Sahu, Electronic Structure and Bonding of Ultralight LiMg, Mater. Sci. Eng. B, 1997, 49, p 74–78.10.1016/S0921-5107(97)00068-8 ↗
25. W.C. Hu, Y. Liu, D.J. Li, X.Q. Zeng, and C.H. Xu, Mechanical and Thermodynamic Properties of Al3Sc and Al3Li Precipitates in Al-Li-Sc Alloys from First-Principles Calculations, Phys. B, 2013, 427, p 85–90.10.1016/j.physb.2013.06.038 ↗
26. M. Al-Fahdi, T. Ouyang, and M. Hu, High-Throughput Computation of Novel Ternary B-C-N Structures and Carbon Allotropes with Electronic-Level Insights into Superhard Materials from Machine Learning, J. Mater. Chem. A, 2021, 9, p 27596–27614.10.1039/D1TA07553E ↗
27. R. Hill, The Elastic Behavior of a Crystalline Aggregate, Proc. Phys. Soc. A, 1952, 65, p 349–357.10.1088/0370-1298/65/5/307 ↗
28. J.P. Watt and L. Peselnick, Clarification of the Hashin–Shtrikman Bounds on the Effective Elastic Moduli of Polycrystals with Hexagonal, Trigonal, and Tetragonal Symmetries, J. Appl. Phys., 1980, 51, p 1525–1531.10.1063/1.327804 ↗
29. M.J. Pang, Y.Z. Zhan, and H.Z. Wang, Ab Initio Investigation into the Structural, Electronic and Elastic Properties of AlCu2TM (TM = Ti, Zr and Hf) Ternary Compounds, Curr. Appl. Phys., 2012, 12, p 957–962.10.1016/j.cap.2011.12.018 ↗
30. D.Q. Sun, Y.X. Wang, X.Y. Zhang, M.Y. Zhang, and Y.F. Niu, First-Principles Calculation on the Thermodynamic and Elastic Properties of Precipitations in Al-Cu Alloys, Superlattices Microstruct., 2016, 100, p 112–119.10.1016/j.spmi.2016.09.012 ↗
31. H.C. Chen, L.J. Yang, and J.P. Long, First-Principles Investigation of the Elastic, Vickers Hardness and Thermodynamic Properties of Al-Cu Intermetallic Compounds, Superlattices Microstruct., 2015, 79, p 156–165.10.1016/j.spmi.2014.11.005 ↗
32. S.F. Pugh, XCII. Relations Between the Elastic Moduli and the Plastic Properties of Polycrystalline Pure Metals, Philos. Mag., 2009, 45, p 823–843.10.1080/14786440808520496 ↗
33. D. Connétable and O. Thomas, First-Principles Study of the Structural, Electronic, Vibrational, and Elastic Properties of Orthorhombic NiSi, Phys. Rev. B, 2009, 79, p 094101.10.1103/PhysRevB.79.094101 ↗
34. R. Dutta and N. Mandal, Effects of Pressure on the Elasticity and Stability of Zircon (ZrSiO4): First-Principle Investigations, Comput. Mater. Sci., 2012, 54, p 157–164.10.1016/j.commatsci.2011.09.035 ↗
35. S.I. Ranganathan and M. Ostoja-Starzewshi, Universal Elastic Anisotropy Index, Phys. Rev. Lett., 2008, 101, p 055504.10.1103/PhysRevLett.101.055504 ↗
36. X. Jiang, J. Zhao, A. Wu, Y. Bai, and X. Jiang, Mechanical and Electronic Properties of B12-Based Ternary Crystals of Orthorhombic Phase, J. Phys. Condens. Matter, 2010, 22, p 315503.10.1088/0953-8984/22/31/315503 ↗
37. J. Yang, J. Long, L. Yang, D. Li, and C. Liu, Theoretical Investigations of the Elastic, Electronic, Optical and Thermodynamics Properties of SrSi, Solid State Sci., 2013, 22, p 71–76.10.1016/j.solidstatesciences.2013.05.006 ↗
38. D.G. Cahill and R.O. Pohl, Lattice Vibrations and Heat Transports in Crystals and Glasses, Annu. Rev. Phys. Chem., 1988, 39, p 93–121.10.1146/annurev.pc.39.100188.000521 ↗
39. M.E. Fine, L.D. Brown, and H.L. Marcus, Elastic Constants Versus Melting Temperature in Metals, Scr. Metall., 1984, 18, p 951–956.10.1016/0036-9748(84)90267-9 ↗
40. E.S. Toberer, A. Zevalkink, and G.J. Snyder, Phonon Engineering through Crystal Chemistry, J. Mater. Chem., 2011, 21, p 15843–15852.10.1039/c1jm11754h ↗
41. D. Roundy, C.R. Krenn, M.L. Cohen, and J.W. Morris Jr., Ideal Shear Strengths of fcc Aluminum and Copper, Phys. Rev. Lett., 1999, 82, p 2713–2716.10.1103/PhysRevLett.82.2713 ↗
42. L.F. Mondolfo, Aluminum Alloys-Structure and Properties, Butterworths and Co, London, 1976.
43. Z.R. Liu, J.H. Chen, S.B. Wang, D.W. Yuan, M.J. Yin, and C.L. Wu, The Structure and the Properties of S-Phase in AlCuMg Alloys, Acta Mater., 2011, 59, p 7396–7405.10.1016/j.actamat.2011.08.009 ↗
44. J. Zhang, Y.N. Huang, C. Mao, and P. Peng, Structural, Elastic and Electronic Properties of θ (Al2Cu) and S (Al2CuMg) Strengthening Precipitates in Al-Cu-Mg Series Alloys: First-Principles Calculations, Solid State Commun., 2012, 152, p 2100–2104.10.1016/j.ssc.2012.09.003 ↗
45. X.Z. Pang, W.C. Yang, J.B. Yang, M.J. Pang, and Y.Z. Zhan, Atomic Structure, Stability and Electronic Properties of S(Al2CuMg)/Al Interface: A First-Principles Study, Intermetallics, 2018, 93, p 329–337.10.1016/j.intermet.2017.10.014 ↗
46. C.M. Li, S.M. Zeng, Z.Q. Chen, N.P. Cheng, and T.X. Chen, First-Principles Calculations of Elastic and Thermodynamic Properties of the Four Main Intermetallic Phases in Al-Zn-Mg-Cu Alloys, Comput. Mater. Sci., 2014, 93, p 210–220.10.1016/j.commatsci.2014.06.031 ↗
47. Y.F. Feng, X. Chen, Y.Y. Li, W.H. You, X.Z. Li, and B. Chen, First-Principles Calculations to Investigate Mechanical, Thermodynamic and Electronic Properties of Al2CuMg Intermetallic Compound under Pressure Effect, Comput. Condens. Matter, 2022, 33, p 00738.10.1016/j.cocom.2022.e00738 ↗
48. C. Zhang, Y.J. Wan, W.J. Zou, X. Shang, and Y.B. Zhang, Composition Optimization for Al-Zn-Mg-Cu Alloys Based on Thermodynamics and First-Principles Calculations, Comput. Theor. Chem., 2021, 1201, p 113293.10.1016/j.comptc.2021.113293 ↗
49. Y.X. Zhang, Y.X. Wang, Z. Chen, and D.Q. Sun, Thermodynamics and Elastic Properties of Precipitates with Orthorhombic, Hexagonal and Cubic in Al-Li-Cu-Mg Alloys, Int. J. Metall. Met. Phys., 2018, 3, p 011.10.35840/2631-5076/9211 ↗