-
金属玻璃(MGs)的剪切带行为与其微观结构不均匀性密切相关. 传统分子动力学(MD)模拟因超快冷却速率导致MGs结构保留了更多液体特征, 而交换原子的蒙特卡洛(SMC)方法能够在模拟上制备出可匹配实验室时间冷却速度的低能态金属玻璃. 本研究通过SMC结合MD方法, 构建软硬相分布可控的Cu50Zr50金属玻璃样品, 揭示纳米尺度结构不均匀性对剪切带萌生与扩展的调控机制. 由MD制备的软相中二十面体有序团簇含量较少, 优先激活塑性事件, 促进应力重新分布, 与邻近硬相一起响应对剪切带扩展起协同作用. MC制备的硬相区由于其高密度的二十面体团簇的含量, 使得应力局域集中, 形成窄剪切带. 通过调控硬相体积分数, 复合样品发生由韧到脆转变. 此外, 在保持硬相百分比不变的前提下, 不同序构策略可以改变非晶的力学行为: 离散硬相的分布能够增加样品的稳定性, 推迟剪切带的产生; 硬相包围软相的策略能够促进样品中产生二次剪切带, 使得剪切带区域相对非局域化. 该研究结果揭示了软硬区结构不均匀性对非晶合金力学性能的影响, 以及为采用序构方法设计金属玻璃力学性能提供了可能的理论指导.Shear banding behavior of metallic glasses (MGs) strongly correlates with the microstructural heterogeneity. Understanding how the nucleation and propagation of shear bands governed by the nanoscale structural heterogeneity are crucial for designing high-performance MGs. Herein, we utilized conventional Molecular dynamics (MD) and swap Monte Carlo (SMC) simulations to construct two phases of CuZr metallic glasses: one the soft phase with a high cooling rate about 1013 K/s, and the other one the hard phase with a extremely low cooling rate in simulations about 104 K/s. The soft phase is more prone to the plastic deformation due to the poor population of icosahedral clusters; the hard phase is of more icosahedral clusters, promoting shear localization once the shear bands form inside. We found a ductile-to-brittle transition in the soft-and-hard phase ordered MGs with the increment of the hard-region fraction c. Additionally, the strategy of how to order these two phases could strongly affect the mechanical behavior of MGs. Dispersive and isolated hard-regions can enhance the mechanical stability of MGs, delaying the occurrence of shear banding. Instead, surrounding soft regions by hard regions can induce a secondary shear band that formed through the reorientation of plastic zones under constrained deformation, leading to a relatively more delocalized plastic deformation zones. The work unveils that the structural heterogeneity achieved by tuning the topology of soft and hard phase can significantly change the mechanical performance of MGs, and this could guide the design of metallic glasses with controllable structures via architectural ordering strategies.
-
Keywords:
- Metallic glasses /
- Order modulation /
- Molecular-dynamics simulations /
- Shear banding
-
图 1 不同软硬区序构$ \mathrm Cu_{50}Zr_{50} $MGs的应力-应变曲线, 插图是屈服应力随c的曲线 (a) 硬区位于样品中间的一个球形区域; (b) 硬区位于样品中心的八个分开的球形区域; (c) 软区位于样品中间的一个球形区域; (d) 软区位于样品中心的八个分开的球形区域
Fig. 1. Stress-strain curves of the soft-hard regions ordered $ \mathrm Cu_{50}Zr_{50} $ MGs, with the inset showing the c-dependent yield stress. (a) The hard region locating at the center as a spherical shape; (b) The hard region locating at the center as eight spherical zones; (c) The soft region locating at the center as a spherical shape; (d) The soft region locating at the center as eight spherical zones.
图 2 四组序构$ \mathrm Cu_{50}Zr_{50} $金属玻璃中$\langle 0,\ 0,\ 12,\ 0\rangle $和局域五次对称性参数(FFLS)随硬相含量c的变化 (a) $\langle 0,\ 0,\ 12,\ 0\rangle $含量; (b) FFLS含量; (c—f) FFLS分布
Fig. 2. Evolution of $\langle 0,\ 0,\ 12,\ 0\rangle $ and the FFLS parameters with c in the ordered $ \mathrm Cu_{50}Zr_{50} $ MGs: (a) $\langle 0,\ 0,\ 12,\ 0\rangle $ clusters; (b) the FFLS parameters; (c–f) the FFLS distribution.
图 3 沿z方向平均的原子非仿射位移量及x-y平面上二十面体团簇空间分布: $ c = 0{\text{%}} $样品, (a) 临界应变$ \gamma_c $前非仿射形变量图, (b) 临界应变$ \gamma_c $后非仿射形变量图, 以及 (c) 二十面体团簇的分布; $ c = 100{\text{%}} $样品, (a) 临界应变$ \gamma_c $前非仿射形变量图, (b) 临界应变$ \gamma_c $后非仿射形变量图, 以及 (c) 二十面体团簇的分布
Fig. 3. Spatial distribution of the non-affine displacement field and icosahedral clusters: (a) and (b) $ \mathrm D^2 $ distribution before and after the critical strain at $ c = 0{\text{{\text{%}}}} $, respectively; (d) and (e) $ \mathrm D^2 $ distribution before and after the critical strain at $ c = 100{\text{{\text{%}}}} $, respectively; (c) and (f) the icosahedral cluster distribution at $ c = 0{\text{{\text{%}}}} $ and $ c = 100{\text{{\text{%}}}} $, respectively.
图 4 四组序构样品(c均为$ 90{\text{%}} $)在临界应变前后非仿射位移量的分布 (a—b) Group1临界应变前后, (c—d) Group2临界应变前后, (e—f) Group3临界应变前后, (g—h) Group4临界应变前后分布情况
Fig. 4. The two-dimensional $ \mathrm D^2 $ distribution of the ordered MGs with c = 90%: (a–b) distribution before and after $ \gamma_c $ in Group1, (c–d) distribution before and after $ \gamma_c $ in Group2, (e–f) distribution before and after $ \gamma_c $ in Group3, (g–h) distribution before and after $ \gamma_c $ in Group4.
图 5 硬区浓度为$ 90{\text{%}} $样品二次剪切带产生过程中非仿射位移的空间分布情况 (a—d) Group3样品中应变分别为0.336, 0.352, 0.360和0.368时的非仿射形变场; (e—h) Group4样品中应变分别为0.336, 0.352, 0.360和0.368时的非仿射形变场
Fig. 5. The two-dimensional $ \mathrm D^2 $ distribution of MGs samples with $ c = 90{\text{%}} $ in the strain range of the secondary shear band: (a–d) strain at 0.336, 0.352, 0.360, and 0.368 for Group3 samples; (e–h) strain at 0.336, 0.352, 0.360, and 0.368 for Group4 samples.
图 6 硬相含量较多的样品中产生一次剪切带和二次剪切带示意图 (a1—a2) 应变较小时软区诱导第一次剪切带产生示意图; (b1—b3) 应变较大时软区诱导第二次剪切带产生示意图; (c1—c2)和 (d1—d3) 分别为硬区较为离散情况下第一次和第二次剪切带产生示意图
Fig. 6. Schematic diagrams for the generation of the primary shear band and the secondary shear band: (a1–a2) the emergency of the primary shear band at relative small strains; (b1–b3) the emergency of the secondary shear band at relative large strains; (c1–c2) and (d1–d3) are the emergency of the primary and the sendary shear bands when the hard-phase regions are dispersively distributed, respectively.
-
[1] Wang W H, Dong C, Shek C H 2004 Mater. Sci. Eng. R-Rep. 44 45
Google Scholar
[2] Schuh C A, Hufnagel T C, Ramamurty U 2007 Acta Mater. 55 4067
Google Scholar
[3] Kruzic J J 2016 Adv. Eng. Mater. 18 1308
Google Scholar
[4] Cheng Y Q, Ma E 2011 Prog. Mater. Sci. 56 379
Google Scholar
[5] Zhu F, Hirata A, Liu P, Song S X, Tian Y, Han J H, Fujita T, Chen M W 2017 Phys. Rev. Lett. 119 215501
Google Scholar
[6] Argon A S 1979 Acta Metall. 27 47
Google Scholar
[7] Falk M L, Langer J S 1998 Phys. Rev. E 57 7192
Google Scholar
[8] Priezjev N V 2017 Phys. Rev. E 95 023002
Google Scholar
[9] Cubuk E D, Ivancic R J S, Schoenholz S S, Strickland D J, Basu A, Davidson Z S, Fontaine J, Hor J L, Huang Y R, Jiang Y, Keim N C, Koshigan K D, Lefever J A, Liu T, Ma X G, Magagnosc D J, Morrow E, Ortiz C P, Rieser J M, Shavit A, Still T, Xu Y, Zhang Y, Nordstrom K N, Arratia P E, Carpick R W, Durian D J, Fakhraai Z, Jerolmack D J, Lee D, Li J, Riggleman R, Turner K T, Yodh A G, Gianola D S, Liu A J 2017 Science 358 1033
Google Scholar
[10] Qiao J C, Wang Q, Pelletier J M, Kato H, Casalini R, Crespo D, Pineda E, Yao Y, Yang Y 2019 Prog. Mater. Sci. 104 250
Google Scholar
[11] 王峥, 汪卫华 2017 物理学报 66 176103
Google Scholar
Wang Z, Wang W H 2017 Acta Phys. Sin. 66 176103
Google Scholar
[12] Chang C, Zhang H P, Zhao R, Li F C, Luo P, Li M Z, Bai H Y 2022 Nat. Mater. 21 1240
Google Scholar
[13] Wang Q, Shang Y H, Yang Y 2023 Mater. Futures 2 017501
Google Scholar
[14] Lu X Q, Feng S D, Li L, Wang L M, Liu R P 2023 J. Phys. Chem. Lett. 14 6998
Google Scholar
[15] Vollmayr K, Kob W, Binder K 1996 J. Chem. Phys. 105 4714
Google Scholar
[16] Liu Y, Bei H, Liu C T, George E P 2007 Appl. Phys. Lett. 90 071909
Google Scholar
[17] Zhang Y, Zhang F, Wang C Z, Mendelev M I, Kramer M J, Ho K M 2015 Phys. Rev. B 91 064105
Google Scholar
[18] Ryltsev R E, Klumov B A, Chtchelkatchev N M, Shunyaev K Y 2016 J. Chem. Phys. 145 034506
Google Scholar
[19] J. A, Bouchbinder E, Procaccia I 2013 Phys. Rev. E 87 042310
[20] Fan M, Wang M L, Zhang K, Liu Y H, Schroers J, Shattuck M D, O’Hern C S 2017 Phys. Rev. E 95 022611
Google Scholar
[21] Sadigh B, Erhart P, Stukowski A, Caro A, Martinez E, Zepeda-Ruiz L 2012 Phys. Rev. B 85 184203
Google Scholar
[22] Grigera T S, Parisi G 2001 Phys. Rev. E 63 045102
[23] Berthier L, Coslovich D, Ninarello A, Ozawa M 2016 Phys. Rev. Lett. 116 238002
Google Scholar
[24] Ninarello A, Berthier L, Coslovich D 2017 Phys. Rev. X 7 021039
[25] Parmar A D S, Ozawa M, Berthier L 2020 Phys. Rev. Lett. 125 085505
Google Scholar
[26] Zhang Z, Ding J, Ma E 2022 Proc. Natl. Acad. Sci. U.S.A. 119 e2213941119
Google Scholar
[27] Yu J H, Zhang Z, Sha Z D, Ding J, Greer A L, Ma E Proc. Natl. Acad. Sci. U.S.A. 122 e2427082122
[28] Luo Q, Cui W R, Zhang H P, Li L L, Shao L L, Cai M J, Zhang Z G, Xue L, Shen J, Gong Y, Li X D, Li M Z, Shen B L 2023 Mater. Futures 2 025001
Google Scholar
[29] Mendelev M I, Kramer M J, Ott R T, Sordelet D J, Yagodin D, Popel P 2009 Philos. Mag. 89 967
Google Scholar
[30] Sadigh B, Erhart P 2012 Phys. Rev. B 86 134204
Google Scholar
[31] Maloney C E, Lemaître A 2006 Phys. Rev. E 74 016118
Google Scholar
[32] Plimpton S 1995 J. Comput. Phys. 117 1
Google Scholar
[33] Barlow H J, Cochran J O, Fielding S M 2020 Phys. Rev. Lett. 125 168003
Google Scholar
[34] Cui S H, Liu H S, Peng H L 2022 Phys. Rev. E 106 014607
Google Scholar
[35] Dasgupta R, Mishra P, Procaccia I, Samwer K 2013 Appl. Phys. Lett. 102 191904
Google Scholar
[36] Liu Y, Liu H S, Peng H L 2023 J. Non-Cryst. Solids 601 122052
Google Scholar
[37] Liu Y, Yan Z H, Liu H S, Shang B S, Peng H L 2024 Phys. Rev. B 109 054115
[38] 李茂枝 2017 物理学报 66 176107
Google Scholar
Li M Z 2017 Acta Phys. Sin. 66 176107
Google Scholar
[39] Peng H L, Li M Z, Wang W H 2011 Phys. Rev. Lett. 106 135503
Google Scholar
[40] Eshelby J D 1957 Proc. R. Soc. London, Ser. A 241 376
Google Scholar
[41] Tang X C, Deng J R, Meng L Y, Yao X H 2025 Int. J. Plast. 189 104323
Google Scholar
计量
- 文章访问数: 427
- PDF下载量: 9
- 被引次数: 0