REFERENCES
1. Zhuo, X.; Wu, Y.; Ju, J.; et al. Recent progress of novel biodegradable zinc alloys: from the perspective of strengthening and toughening. J. Mater. Res. Technol. 2022, 17, 244-69.
2. Liu, Y.; Zeng, Z.; Gong, H.; et al. Work softening behavior and microstructure evolution of Zn-0.12Cu-0.08Ti alloy during cold rolling. J. Alloys. Compd. 2025, 1032, 180998.
3. Li, R.; Ding, Y.; Zhang, H.; Wang, X.; Gao, Y.; Xu, J. Toward high strength and large strain hardening Zn alloys via a novel multiscale-heterostructure strategy. Mater. Sci. Eng. A. 2024, 899, 146410.
4. Song, Z.; Niu, R.; Cui, X.; et al. Mechanism of room-temperature superplasticity in ultrafine-grained Al-Zn alloys. Acta. Mater. 2023, 246, 118671.
5. Hou, M.; Deng, K.; Wang, C.; Nie, K.; Shi, Q. The balance between work hardening and softening behaviors of Mg-xZn-1Gd-0.2Ca-0.1Zr alloys influenced by trace Zn addition. J. Alloys. Compd. 2023, 969, 172379.
6. Zhao, L.; Xia, W.; Yan, H.; Chen, J.; Su, B. Effects of Zn addition on dynamic recrystallization of high strain rate rolled Al-Mg sheets. Met. Mater. Int. 2021, 28, 1264-76.
7. Wu, C.; Lin, F.; Liu, H.; et al. Stronger and coarser-grained biodegradable zinc alloys. Nature 2025, 638, 684-9.
8. Bednarczyk, W.; Kawałko, J.; Wątroba, M.; Szuwarzyński, M.; Bała, P. Investigation of slip systems activity and grain boundary sliding in fine-grained superplastic zinc alloy. Archiv.Civ.Mech.Eng. 2023, 23, 253.
9. Cantwell, P. R.; Tang, M.; Dillon, S. J.; Luo, J.; Rohrer, G. S.; Harmer, M. P. Grain boundary complexions. Acta. Mater. 2014, 62, 1-48.
10. Wei, J.; Feng, B.; Ishikawa, R.; et al. Direct imaging of atomistic grain boundary migration. Nat. Mater. 2021, 20, 951-5.
11. Schweizer, P.; Sharma, A.; Pethö, L.; et al. Atomic scale volume and grain boundary diffusion elucidated by in situ STEM. Nat. Commun. 2023, 14, 7601.
12. Zhang, X.; Wang, D.; Nagaumi, H.; et al. Accelerating the design of highly separable Fe‐containing intermetallics in Al-Si alloys via DFT calculations and experimental validation. Mater. Genome. Eng. Adv. 2025, 3, e70008.
13. Yang, P.; Li, S.; Xie, H.; et al. Two-dimensional interface superstructures assembled by well-ordered solute atoms. J. Mater. Sci. Technol. 2023, 142, 253-9.
14. Kosarev, I.; Shcherbinin, S.; Kistanov, A.; Babicheva, R.; Korznikova, E.; Dmitriev, S. An approach to evaluate the accuracy of interatomic potentials as applied to tungsten. Comput. Mater. Sci. 2024, 231, 112597.
15. Mishin, Y. Machine-learning interatomic potentials for materials science. Acta. Mater. 2021, 214, 116980.
16. Nie, J.; Hu, C.; Yan, Q.; Luo, J. Discovery of electrochemically induced grain boundary transitions. Nat. Commun. 2021, 12, 2374.
17. Behler, J. Perspective: machine learning potentials for atomistic simulations. J. Chem. Phys. 2016, 145, 170901.
18. Zhang, L.; Han, J.; Wang, H.; Car, R.; E, W. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys. Rev. Lett. 2018, 120, 143001.
19. Deringer, V. L.; Bernstein, N.; Csányi, G.; et al. Origins of structural and electronic transitions in disordered silicon. Nature 2021, 589, 59-64.
20. Zeng, J.; Cao, L.; Xu, M.; Zhu, T.; Zhang, J. Z. H. Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation. Nat. Commun. 2020, 11, 5713.
21. Jia, W.; Wang, H.; Chen, M.; et al. Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, Atlanta, GA, USA, November 9-19, 2020; IEEE: New York, NY, USA, 2020; pp 1-14.
22. Su, H.; Zheng, S.; Yang, Z.; Wang, J.; Ye, H. Atomic-scale insights into grain boundary-mediated plasticity mechanisms in a magnesium alloy subjected to cyclic deformation. Acta. Mater. 2024, 277, 120210.
23. Du, C.; Gao, Y.; Zha, M.; Wang, C.; Jia, H.; Wang, H. Deformation-induced grain rotation and grain boundary formation achieved through dislocation-disclination reactions in polycrystalline hexagonal close-packed metals. Acta. Mater. 2023, 250, 118855.
24. Ma, Z. C.; Tang, X. Z.; Mao, Y.; Guo, Y. F. The plastic deformation mechanisms of hcp single crystals with different orientations: molecular dynamics simulations. Materials 2021, 14, 733.
25. Gou, W.; Shi, Z. Z.; Zhu, Y.; et al. Multi‐objective optimization of three mechanical properties of Mg alloys through machine learning. Mater. Genome. Eng. Adv. 2024, 2, e54.
26. Della Ventura, N. M.; Sharma, A.; Cayron, C.; et al. Response of magnesium microcrystals to c-axis compression and contraction loadings at low and high strain rates. Acta. Mater. 2023, 248, 118762.
27. Liu, B. Y.; Zhang, Z.; Liu, F.; et al. Rejuvenation of plasticity via deformation graining in magnesium. Nat. Commun. 2022, 13, 1060.
28. Fang, Q.; Sansoz, F. Columnar grain-driven plasticity and cracking in nanotwinned FCC metals. Acta. Mater. 2021, 212, 116925.
29. Chang, Z.; Feng, L.; Xue, H. T.; et al. Deep-learning potential molecular dynamics study on nanopolycrystalline Al-Er alloys: effects of Er concentration, grain boundary segregation, and grain size on plastic deformation. J. Chem. Inf. Model. 2025, 65, 3282-93.
30. Wang, H. Molecular modeling by machine learning. Math. Numer. Sin. 2021, 43. , 261-78. (in Chinese).
31. Wu, J.; Huang, A.; Xie, H. P.; et al. Multi-scale simulation of mechanical and thermal transport properties of materials based on machine learning potential. J. Chin. Ceram. Soc. 2023, 51, 531-43. (in Chinese).
32. Zhang, Y.; Wang, H.; Chen, W.; et al. DP-GEN: a concurrent learning platform for the generation of reliable deep learning based potential energy models. Comput. Phys. Commun. 2020, 253, 107206.
33. Wang, H.; Zhang, L.; Han, J.; E, W. DeePMD-kit: a deep learning package for many-body potential energy representation and molecular dynamics. Comput. Phys. Commun. 2018, 228, 178-84.
34. Kresse, G.; Hafner, J. Ab initio molecular dynamics for liquid metals. Phys. Rev. B. Condens. Matter. 1993, 47, 558-61.
35. Kresse, G.; Hafner, J. Ab initio molecular dynamics for open-shell transition metals. Phys. Rev. B. Condens. Matter. 1993, 48, 13115-8.
36. Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 1996, 77, 3865-8.
37. Monkhorst, H. J.; Pack, J. D. Special points for Brillouin-zone integrations. Phys. Rev. B. 1976, 13, 5188-92.
38. Xu, C.; Tian, X.; Jiang, W.; Wang, Q.; Fan, H. Atomistic migration mechanisms of
39. Tschopp, M. A.; Mcdowell, D. L. Structures and energies of Σ3 asymmetric tilt grain boundaries in copper and aluminium. Philos. Mag. 2007, 87, 3147-73.
40. Hirel, P. Atomsk: a tool for manipulating and converting atomic data files. Comput. Phys. Commun. 2015, 197, 212-9.
41. Brostow, W.; Dussault, J.; Fox, B. L. Construction of Voronoi polyhedra. J. Comput. Phys. 1978, 29, 81-92.
42. Plimpton, S. Fast parallel algorithms for short-range molecular dynamics. J. Comput. Phys. 1995, 117, 1-19.
43. Stukowski, A. Visualization and analysis of atomistic simulation data with OVITO-the Open Visualization Tool. Modelling. Simul. Mater. Sci. Eng. 2010, 18, 015012.
44. Jang, H.; Kim, K.; Lee, B. Modified embedded-atom method interatomic potentials for pure Zn and Mg-Zn binary system. Calphad 2018, 60, 200-7.
45. Wang, J.; Beyerlein, I. J. Atomic structures of
46. Liu, X.; Adams, J. B.; Ercolessi, F.; Moriarty, J. A. EAM potential for magnesium from quantum mechanical forces. Model. Simul. Mater. Sci. Eng. 1996, 4, 293-303.
47. Agrawal, A.; Mishra, R.; Ward, L.; Flores, K. M.; Windl, W. An embedded atom method potential of beryllium. Model. Simul. Mater. Sci. Eng. 2013, 21, 085001.
48. Nitol, M. S.; Dickel, D. E.; Barrett, C. D. Artificial neural network potential for pure zinc. Comput. Mater. Sci. 2021, 188, 110207.
49. Mei, H.; Cheng, L.; Chen, L.; Wang, F.; Li, J.; Kong, L. Development of machine learning interatomic potential for zinc. Comput. Mater. Sci. 2024, 233, 112723.
50. Kittel, C. Introduction to solid state physics, 8th ed.; Wiley, 2004. https://www.wiley.com/en-us/Introduction+to+Solid+State+Physics%2C+8th+Edition-p-9780471415268. (accessed 2026-04-22).
51. Ledbetter, H. M. Elastic properties of zinc: a compilation and a review. J. Phys. Chem. Ref. Data. 1977, 6, 1181-203.
53. Togo, A.; Tanaka, I. First principles phonon calculations in materials science. Scr. Mater. 2015, 108, 1-5.
54. Chaput, L.; Togo, A.; Tanaka, I.; Hug, G. Phonon-phonon interactions in transition metals. Phys. Rev. B. 2011, 84, 094302.
55. Gajdoš, M.; Hummer, K.; Kresse, G.; Furthmüller, J.; Bechstedt, F. Linear optical properties in the projector-augmented wave methodology. Phys. Rev. B. 2006, 73, 045112.
56. Baroni, S.; Giannozzi, P.; Testa, A. Green’s-function approach to linear response in solids. Phys. Rev. Lett. 1987, 58, 1861-4.
57. Gonze, X.; Lee, C. Dynamical matrices, Born effective charges, dielectric permittivity tensors, and interatomic force constants from density-functional perturbation theory. Phys. Rev. B. 1997, 55, 10355-68.
58. Gengor, G.; Mohammed, A. S. K.; Sehitoglu, H.
59. Kumar, A.; Wang, J.; Tomé, C. N. First-principles study of energy and atomic solubility of twinning-associated boundaries in hexagonal metals. Acta. Mater. 2015, 85, 144-54.
60. Lu, D.; Jiang, W.; Chen, Y.; et al. DP compress: a model compression scheme for generating efficient Deep Potential models. J. Chem. Theory. Comput. 2022, 18, 5559-67.
61. Tsuzuki, H.; Branicio, P. S.; Rino, J. P. Structural characterization of deformed crystals by analysis of common atomic neighborhood. Comput. Phys. Commun. 2007, 177, 518-23.
62. Sun, H.; Singh, C. V. Temperature dependence of grain boundary excess free volume. Scr. Mater. 2020, 178, 71-6.
63. Wang, H.; Jin, J.; Wang, D.; et al. Energetic and structure characteristics of the
64. Baskes, M. I.; Johnson, R. A. Modified embedded atom potentials for HCP metals. Model. Simul. Mater. Sci. Eng. 1994, 2, 147-63.
