Atomistic simulations on liquid Mg–Sr alloys assisted with deep learning potential
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Hydrogen Storage and Materials · Machine Learning in Materials Science · Thermodynamic and Structural Properties of Metals and Alloys
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (40)
Show source-provided references
1. Zhao MC, Zhao YC, Yin DF, Wang S, Shangguan YM, Liu C, Tan LL, Shuai CJ, Yang K, Atrens A (2019) Biodegradation behavior of coated as-extruded Mg–Sr alloy in simulated body fluid. Acta Metallurgica Sinica-Engl Lett 32:1195–1206. https://doi.org/10.1007/s40195-019-00892-510.1007/s40195-019-00892-5 ↗
2. Yang Y, Xiong XM, Chen J, Peng XD, Chen DL, Pan FS (2023) Research advances of magnesium and magnesium alloys worldwide in 2022. J Magnes Alloys 11:2611–2654. https://doi.org/10.1016/j.jma.2023.07.01110.1016/j.jma.2023.07.011 ↗
3. Liu LZ, Chen XH, Pan FS (2021) A review on electromagnetic shielding magnesium alloys. J Magnes Alloys 9:1906–1921. https://doi.org/10.1016/j.jma.2021.10.00110.1016/j.jma.2021.10.001 ↗
4. Lee JY, Lee JM, Son KS, J-i J, Cho YH (2023) A study on the interaction between a Sr modifier and an Al-5Ti-1B grain refiner in an Al-7Si-0.35Mg casting alloy. J Alloys Compd 938:168598. https://doi.org/10.1016/j.jallcom.2022.16859810.1016/j.jallcom.2022.168598 ↗
5. Ghorbani F, Emamy M, Mirzadeh H (2021) Enhanced tensile properties of as-cast Mg-10Al magnesium alloy via strontium addition and hot working. Arch Civ Mech Eng 21:86. https://doi.org/10.1007/s43452-021-00241-310.1007/s43452-021-00241-3 ↗
6. Ganesh MS, Reghunath N, Levin JM, Prasad A, Doondi S, Shankar KV (2022) Strontium in Al–Si–Mg alloy: a review. Met Mater Int. https://doi.org/10.1007/s12540-021-01054-y10.1007/s12540-021-01054-y ↗
7. Dargusch MS, Shi Z, Zhu H, Atrens A, Song GL (2021) Microstructure modification and corrosion resistance enhancement of die-cast Mg–Al–Re alloy by Sr alloying. J Magnes Alloys 9:950–963. https://doi.org/10.1016/j.jma.2020.09.00810.1016/j.jma.2020.09.008 ↗
8. Kiani F, Lin J, Vahid A, Munir K, Wen C, Li Y (2022) Mechanical and corrosion properties of extruded Mg–Zr–Sr alloys for biodegradable implant applications. Mater Sci Eng A 831:142192. https://doi.org/10.1016/j.msea.2021.14219210.1016/j.msea.2021.142192 ↗
9. Ye W, Zhou Q, Shi Y, Xie M, Chen B, Wang H, Liu W (2024) Robust wear performance of graphene-reinforced high entropy alloy composites. Carbon 224:119040. https://doi.org/10.1016/j.carbon.2024.11904010.1016/j.carbon.2024.119040 ↗
10. Ren Y, Zhou Q, Hua D, Huang Z, Li Y, Jia Q, Gumbsch P, Greiner C, Wang H, Liu W (2024) Wear-resistant CoCrNi multi-principal element alloy at cryogenic temperature. Sci Bull 69:227–236. https://doi.org/10.1016/j.scib.2023.12.00310.1016/j.scib.2023.12.003 ↗
11. Hua D, Zhou Q, Shi Y, Li S, Hua K, Wang H, Li S, Liu W (2023) Revealing the deformation mechanisms of <110> symmetric tilt grain boundaries in CoCrNi medium-entropy alloy. Int J Plast 171:103832. https://doi.org/10.1016/j.ijplas.2023.10383210.1016/j.ijplas.2023.103832 ↗
12. Guo CP, Du ZM (2004) Thermodynamic assessment of the La–Mg system. J Alloys Compd 385:109–113. https://doi.org/10.1016/j.jallcom.2004.04.10510.1016/j.jallcom.2004.04.105 ↗
13. Nayeb-Hashemi A, Clark J (1986) The Mg–Sr (Magnesium–Strontium) system. Bull Alloy Phase Diagr 7:149–156. https://doi.org/10.1007/BF0288155410.1007/BF02881554 ↗
14. Sommer F, Predel B, Assmann D (1977) Thermodynamic investigations of liquid alloys in the systems Mg–Ca, Mg–Ba and Mg–Sr. Z METALLKD 68:347–349
15. Sommer F (1980) Determination of thermodynamic activities of liquid alloys in the systems Mg–Sr and Ba–Mg. Z METALLKD 71:120–123
16. Qi Y, Çagin T, Kimura Y, Goddard WA (1999) Molecular-dynamics simulations of glass formation and crystallization in binary liquid metals: Cu–Ag and Cu–Ni. Phys Rev B 59:3527–3533. https://doi.org/10.1103/PhysRevB.59.352710.1103/PhysRevB.59.3527 ↗
17. Zhang W, Peng Y, Liu Z (2014) Molecular dynamics simulations of the melting curve of NiAl alloy under pressure. AIP Adv. https://doi.org/10.1063/1.487651510.1063/1.4876515 ↗
18. Wang S, Kramer M, Xu M, Wu S, Hao S, Sordelet D, Ho K, Wang C (2009) Experimental and ab initio molecular dynamics simulation studies of liquid Al 60 Cu 40 alloy. Phys Rev B 79:144205. https://doi.org/10.1103/PhysRevB.79.14420510.1103/PhysRevB.79.144205 ↗
19. Li J, Xiao R, Qin J, Ruan Y, Li H (2023) The structural exploration of thermodynamics and dynamics in Ti–Ni liquid by ab initio molecular dynamics simulation. Comput Mater Sci 230:112499. https://doi.org/10.1016/j.commatsci.2023.11249910.1016/j.commatsci.2023.112499 ↗
20. Zhang L, Han J, Wang H, Car R (2018) Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys Rev Lett 120:143001. https://doi.org/10.1103/PhysRevLett.120.14300110.1103/PhysRevLett.120.143001 ↗
21. Wen T, Cai-Zhuang Wang MJ, Kramer YS, Ye B, Wang H, Liu X, Zhang C, Zhang F, Ho K-M, Wang N (2019) Development of a deep machine learning interatomic potential for metalloid-containing Pd–Si compounds. Phys Rev B. https://doi.org/10.1103/PhysRevB.100.17410110.1103/PhysRevB.100.174101 ↗
22. Wang Q, Zhai B, Wang HP, Wei B (2021) Atomic structure of liquid refractory Nb5Si3 intermetallic compound alloy based upon deep neural network potential. J Appl Phys 130:185103. https://doi.org/10.1063/5.006715710.1063/5.0067157 ↗
23. Tang L, Yang ZJ, Wen TQ, Ho KM, Kramer MJ, Wang CZ (2020) Development of interatomic potential for Al–Tb alloys using a deep neural network learning method. Phys Chem Chem Phys 22:18467–18479. https://doi.org/10.1039/d0cp01689f10.1039/d0cp01689f ↗
24. Tang L, Ho KM, Wang CZ (2021) Molecular dynamics simulation of metallic Al–Ce liquids using a neural network machine learning interatomic potential. J Chem Phys 155:194503. https://doi.org/10.1063/5.006606110.1063/5.0066061 ↗
25. Zhai B, Wang H (2023) Accurate interatomic potential for the nucleation in liquid Ti–Al binary alloy developed by deep neural network learning method. Comput Mater Sci 216:111843. https://doi.org/10.1016/j.commatsci.2022.11184310.1016/j.commatsci.2022.111843 ↗
26. Zhang Y, Wang H, Chen W, Zeng J, Zhang L, Wang H, Weinan E (2020) DP-GEN: a concurrent learning platform for the generation of reliable deep learning based potential energy models. Comput Phys Commun 253:107206. https://doi.org/10.1016/j.cpc.2020.10720610.1016/j.cpc.2020.107206 ↗
27. Kresse G, Furthmuller J (1996) Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys Rev B Condens Matter 54:11169–11186. https://doi.org/10.1103/physrevb.54.1116910.1103/physrevb.54.11169 ↗
28. Kresse G, Joubert D (1999) From ultrasoft pseudopotentials to the projector augmented-wave method. Phys Rev B 59:1758–1775. https://doi.org/10.1103/PhysRevB.59.175810.1103/PhysRevB.59.1758 ↗
29. Perdew JP, Burke K, Ernzerhof M (1996) Generalized gradient approximation made simple. Phys Rev Lett 77:3865. https://doi.org/10.1103/PhysRevLett.77.386510.1103/PhysRevLett.77.3865 ↗
30. Grimme S, Antony J, Ehrlich S, Krieg H (2010) A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J Chem Phys 132:154104. https://doi.org/10.1063/1.338234410.1063/1.3382344 ↗
31. Plimpton S (1995) Fast parallel algorithms for short-range molecular dynamics. J Comput Phys 117:1–19. https://doi.org/10.1006/jcph.1995.103910.1006/jcph.1995.1039 ↗
32. Le RS, Jund P (2010) Ring statistics analysis of topological networks: New approach and application to amorphous GeS2 and SiO2 systems. Comput Mater Sci 49:70–83. https://doi.org/10.1016/j.commatsci.2010.04.02310.1016/j.commatsci.2010.04.023 ↗
33. Waseda Y (1980) The structure of non-crystalline materials. Liguids and Amorphous Solids
34. Wax JF, Albaki R, Bretonnet JL (2000) Structural and dynamical properties of liquid alkaline-earth metals near the melting point. Phys Rev B 62:14818–14827. https://doi.org/10.1103/PhysRevB.62.1481810.1103/PhysRevB.62.14818 ↗
35. Reijers HT, van der Lugt W, Saboungi ML (1990) Molecular-dynamics study of liquid NaPb, KPb, RbPb, and CsPb alloys. Phys Rev B Condens Matter 42:3395–3405. https://doi.org/10.1103/physrevb.42.339510.1103/physrevb.42.3395 ↗
36. McGonigal P, Kirshenbaum A, Grosse A (1962) The liquid temperature range, density, and critical constants of magnesium1. J Phys Chem 66:737–74010.1021/j100810a038 ↗
37. Bohdansky J, Schins H (1968) Surface tension and density of the liquid earth alkaline metals Mg, Ca, Sr. Ba J Inorg Nucl Chem 30:2331–233710.1016/0022-1902(68)80241-6 ↗
38. Korkmaz SD, Korkmaz S (2007) Atomic transport properties of liquid alkaline earth metals: a comparison of scaling laws proposed for diffusion and viscosity. Modell Simul Mater Sci Eng 15:285–294. https://doi.org/10.1088/0965-0393/15/3/00710.1088/0965-0393/15/3/007 ↗
39. Yokoyama I, Tsuchiya S (2002) Excess entropy, diffusion coefficient, viscosity coefficient and surface tension of liquid simple metals from diffraction data. Mater Trans 43:67–72. https://doi.org/10.2320/matertrans.43.6710.2320/matertrans.43.67 ↗
40. Aljarrah M, Medraj M (2008) Thermodynamic modelling of the Mg-Ca, Mg–Sr, Ca-Sr and Mg-Ca-Sr systems using the modified quasichemical model. Calphad-Comput Coupling Phase Diagr Thermochem 32:240–251. https://doi.org/10.1016/j.calphad.2007.09.00110.1016/j.calphad.2007.09.001 ↗