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Machine learning atomic dynamics to unfold the origin of plasticity in metallic glasses: From thermo- to acousto-plastic flow

机器学习原子运动揭示金属玻璃塑性起源: 从热塑性到超声塑性

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Abstract

Metallic glasses (MGs) have an amorphous atomic arrangement, but their structure and dynamics in the nanoscale are not homogeneous. Numerous studies have confirmed that the static and dynamic heterogeneities of MGs are vital for their deformation mechanism. The “defects” in MGs are envisaged to be structurally loosely packed and dynamically active to external stimuli. To date, no definite structure-property relationship has been established to identify liquid-like “defects” in MGs. In this paper, we proposed a machine-learned “defects” from atomic trajectories rather than static structural signatures. We analyzed the atomic motion behavior at different temperatures via a k-nearest neighbors machine learning model, and quantified the dynamics of individual atoms as the machine-learned temperature. Applying this new temperature-like parameter to MGs under stress-induced flow, we can recognize which atoms respond like “liquids” to the applied loads. The evolution of liquid-like regions reveals the dynamic origin of plasticity (thermo- and acousto-plasticity) of MGs and the correlation between stress-induced heterogeneity and local environment around atoms, providing new insights into thermo- and acousto-plastic forming.

摘要

金属玻璃具有无序的原子排列, 但其结构与动力学并非各处均匀. 许多研究证实金属玻璃的结构与动态不均匀性对于其塑性机制至关重要. 金属玻璃的“缺陷”被视为结构上疏松排布、 动力学上积极响应外界刺激的区域. 但迄今仍未建立明确的结构-性能关系来甄别金属玻璃中的类液缺陷. 本文中, 我们基于模拟原子运动轨迹并结合机器学习提出了一种不依赖于静态结构特征的缺陷. 利用k近邻机器学习模型分析并预测了不同温度下的原子运动行为, 建立了温度类标签-原子运动特征映射关系. 应用这个“机器学习温度”参数理解金属玻璃在应力下的塑性流, 识别类液区原子. 类液区的演化揭示了金属玻璃塑性的动态起源(包括热塑性和超声塑性), 展示了应力诱发的非均匀性和原子局域环境的关联, 为热塑性成型和超声加工提供了新见解.

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References

  1. Ruta B, Pineda E, Evenson Z. Relaxation processes and physical aging in metallic glasses. J Phys-Condens Matter, 2017, 29: 503002

    Article  CAS  Google Scholar 

  2. Cheng YQ, Ma E. Atomic-level structure and structure-property relationship in metallic glasses. Prog Mater Sci, 2011, 56: 379–473

    Article  CAS  Google Scholar 

  3. Qiao JC, Wang Q, Pelletier JM, et al. Structural heterogeneities and mechanical behavior of amorphous alloys. Prog Mater Sci, 2019, 104: 250–329

    Article  CAS  Google Scholar 

  4. Wang WH. The elastic properties, elastic models and elastic perspectives of metallic glasses. Prog Mater Sci, 2012, 57: 487–656

    Article  CAS  Google Scholar 

  5. Suryanarayana C, Inoue A. Bulk Metallic Glasses. Boca Raton: CRC press, 2017

    Google Scholar 

  6. Yang Y, Zeng JF, Volland A, et al. Fractal growth of the dense-packing phase in annealed metallic glass imaged by high-resolution atomic force microscopy. Acta Mater, 2012, 60: 5260–5272

    Article  CAS  Google Scholar 

  7. Ye JC, Lu J, Liu CT, et al. Atomistic free-volume zones and inelastic deformation of metallic glasses. Nat Mater, 2010, 9: 619–623

    Article  CAS  Google Scholar 

  8. Hu YC, Li FX, Li MZ, et al. Five-fold symmetry as indicator of dynamic arrest in metallic glass-forming liquids. Nat Commun, 2015, 6: 8310

    Article  CAS  Google Scholar 

  9. Tanaka H. Two-order-parameter model of the liquid-glass transition. III. Universal patterns of relaxations in glass-forming liquids. J Non-Crystalline Solids, 2005, 351: 3396–3413

    Article  CAS  Google Scholar 

  10. Wang Z, Sun BA, Bai HY, et al. Evolution of hidden localized flow during glass-to-liquid transition in metallic glass. Nat Commun, 2014, 5: 5823

    Article  CAS  Google Scholar 

  11. Harmon JS, Demetriou MD, Johnson WL, et al. Anelastic to plastic transition in metallic glass-forming liquids. Phys Rev Lett, 2007, 99: 135502

    Article  CAS  Google Scholar 

  12. Debenedetti PG, Stillinger FH. Supercooled liquids and the glass transition. Nature, 2001, 410: 259–267

    Article  CAS  Google Scholar 

  13. Mayr SG. Activation energy of shear transformation zones: A key for understanding rheology of glasses and liquids. Phys Rev Lett, 2006, 97: 195501

    Article  CAS  Google Scholar 

  14. Guan P, Chen M, Egami T. Stress-temperature scaling for steady-state flow in metallic glasses. Phys Rev Lett, 2010, 104: 205701

    Article  CAS  Google Scholar 

  15. Wang Q, Zhang ST, Yang Y, et al. Unusual fast secondary relaxation in metallic glass. Nat Commun, 2015, 6: 7876

    Article  CAS  Google Scholar 

  16. Wang Q, Liu JJ, Ye YF, et al. Universal secondary relaxation and unusual brittle-to-ductile transition in metallic glasses. Mater Today, 2017, 20: 293–300

    Article  CAS  Google Scholar 

  17. Liu XD, Wang TY, Ye YF, et al. Unusual vortex-like atomic motion observed for viscoelasticity in metallic glass. Comput Mater Sci, 2018, 155: 104–111

    Article  CAS  Google Scholar 

  18. Spikes H. Stress-augmented thermal activation: Tribology feels the force. Friction, 2018, 6: 1–31

    Article  Google Scholar 

  19. Johnson WL, Samwer K. A universal criterion for plastic yielding of metallic glasses with a (t/tg)2/3 temperature dependence. Phys Rev Lett, 2005, 95: 195501

    Article  CAS  Google Scholar 

  20. Fan Y, Iwashita T, Egami T. How thermally activated deformation starts in metallic glass. Nat Commun, 2014, 5: 5083

    Article  CAS  Google Scholar 

  21. Rodney D, Schuh C. Distribution of thermally activated plastic events in a flowing glass. Phys Rev Lett, 2009, 102: 235503

    Article  CAS  Google Scholar 

  22. Demetriou MD, Harmon JS, Tao M, et al. Cooperative shear model for the rheology of glass-forming metallic liquids. Phys Rev Lett, 2006, 97: 065502

    Article  CAS  Google Scholar 

  23. Ye YF, Liu XD, Wang S, et al. The kinetic origin of delayed yielding in metallic glasses. Appl Phys Lett, 2016, 108: 251901

    Article  CAS  Google Scholar 

  24. Ma J, Yang C, Liu X, et al. Fast surface dynamics enabled cold joining of metallic glasses. Sci Adv, 2019, 5: eaax7256

    Article  CAS  Google Scholar 

  25. Li X, Wei D, Zhang JY, et al. Ultrasonic plasticity of metallic glass near room temperature. Appl Mater Today, 2020, 21: 100866

    Article  Google Scholar 

  26. Spaepen F. A microscopic mechanism for steady state inhomogeneous flow in metallic glasses. Acta Metall, 1977, 25: 407–415

    Article  CAS  Google Scholar 

  27. Steinhardt PJ, Nelson DR, Ronchetti M. Bond-orientational order in liquids and glasses. Phys Rev B, 1983, 28: 784–805

    Article  CAS  Google Scholar 

  28. Tomida T, Egami T. Molecular-dynamics study of orientational order in liquids and glasses and its relation to the glass transition. Phys Rev B, 1995, 52: 3290–3308

    Article  CAS  Google Scholar 

  29. Yang X, Liu R, Yang M, et al. Structures of local rearrangements in soft colloidal glasses. Phys Rev Lett, 2016, 116: 238003

    Article  CAS  Google Scholar 

  30. Tong H, Tanaka H. Structural order as a genuine control parameter of dynamics in simple glass formers. Nat Commun, 2019, 10: 5596

    Article  CAS  Google Scholar 

  31. Tong H, Tanaka H. Role of attractive interactions in structure ordering and dynamics of glass-forming liquids. Phys Rev Lett, 2020, 124: 225501

    Article  CAS  Google Scholar 

  32. Sheng HW, Luo WK, Alamgir FM, et al. Atomic packing and short-to-medium-range order in metallic glasses. Nature, 2006, 439: 419–425

    Article  CAS  Google Scholar 

  33. Peng HL, Li MZ, Wang WH. Structural signature of plastic deformation in metallic glasses. Phys Rev Lett, 2011, 106: 135503

    Article  CAS  Google Scholar 

  34. Ma E. Tuning order in disorder. Nat Mater, 2015, 14: 547–552

    Article  CAS  Google Scholar 

  35. Peng CX, Şopu D, Song KK, et al. Bond length deviation in CuZr metallic glasses. Phys Rev B, 2017, 96: 174112

    Article  Google Scholar 

  36. Rieser JM, Goodrich CP, Liu AJ, et al. Divergence of Voronoi cell anisotropy vector: A threshold-free characterization of local structure in amorphous materials. Phys Rev Lett, 2016, 116: 088001

    Article  CAS  Google Scholar 

  37. Ding J, Patinet S, Falk ML, et al. Soft spots and their structural signature in a metallic glass. Proc Natl Acad Sci USA, 2014, 111: 14052–14056

    Article  CAS  Google Scholar 

  38. Widmer-Cooper A, Perry H, Harrowell P, et al. Irreversible reorganization in a supercooled liquid originates from localized soft modes. Nat Phys, 2008, 4: 711–715

    Article  CAS  Google Scholar 

  39. Zylberg J, Lerner E, Bar-Sinai Y, et al. Local thermal energy as a structural indicator in glasses. Proc Natl Acad Sci USA, 2017, 114: 7289–7294

    Article  CAS  Google Scholar 

  40. Wei D, Yang J, Jiang MQ, et al. Revisiting the structure-property relationship of metallic glasses: Common spatial correlation revealed as a hidden rule. Phys Rev B, 2019, 99: 014115

    Article  CAS  Google Scholar 

  41. Dyre JC. Colloquium: The glass transition and elastic models of glass-forming liquids. Rev Mod Phys, 2006, 78: 953–972

    Article  CAS  Google Scholar 

  42. Yang J, Wang YJ, Ma E, et al. Structural parameter of orientational order to predict the boson vibrational anomaly in glasses. Phys Rev Lett, 2019, 122: 015501

    Article  CAS  Google Scholar 

  43. Ding J, Cheng YQ, Sheng H, et al. Universal structural parameter to quantitatively predict metallic glass properties. Nat Commun, 2016, 7: 13733

    Article  CAS  Google Scholar 

  44. Sun YT, Bai HY, Li MZ, et al. Machine learning approach for prediction and understanding of glass-forming ability. J Phys Chem Lett, 2017, 8: 3434–3439

    Article  CAS  Google Scholar 

  45. Ward L, Agrawal A, Choudhary A, et al. A general-purpose machine learning framework for predicting properties of inorganic materials. npj Comput Mater, 2016, 2: 16028

    Article  Google Scholar 

  46. Ren F, Ward L, Williams T, et al. Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments. Sci Adv, 2018, 4: eaaq1566

    Article  CAS  Google Scholar 

  47. Liu X, Li X, He Q, et al. Machine learning-based glass formation prediction in multicomponent alloys. Acta Mater, 2020, 201: 182–190

    Article  CAS  Google Scholar 

  48. Zhou ZQ, He QF, Liu XD, et al. Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning. npj Comput Mater, 2021, 7: 138

    Article  CAS  Google Scholar 

  49. Cubuk ED, Schoenholz SS, Rieser JM, et al. Identifying structural flow defects in disordered solids using machine-learning methods. Phys Rev Lett, 2015, 114: 108001

    Article  CAS  Google Scholar 

  50. Wang Q, Jain A. A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses. Nat Commun, 2019, 10: 5537

    Article  CAS  Google Scholar 

  51. Fan Z, Ding J, Ma E. Machine learning bridges local static structure with multiple properties in metallic glasses. Mater Today, 2020, 40: 48–62

    Article  CAS  Google Scholar 

  52. Peng ZH, Yang ZY, Wang YJ. Machine learning atomic-scale stiffness in metallic glass. Extreme Mech Lett, 2021, 48: 101446

    Article  Google Scholar 

  53. Yang ZY, Wei D, Zaccone A, et al. Machine-learning integrated glassy defect from an intricate configurational-thermodynamic-dynamic space. Phys Rev B, 2021, 104: 064108

    Article  CAS  Google Scholar 

  54. Wang Q, Ding J, Zhang L, et al. Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning. npj Comput Mater, 2020, 6: 194

    Article  Google Scholar 

  55. Hirata A, Wada T, Obayashi I, et al. Structural changes during glass formation extracted by computational homology with machine learning. Commun Mater, 2020, 1: 98

    Article  Google Scholar 

  56. Liu X, Li F, Yang Y. “Softness” as the structural origin of plasticity in disordered solids: A quantitative insight from machine learning. Sci China Mater, 2019, 62: 154–160

    Article  Google Scholar 

  57. Mendelev MI, Kramer MJ, Ott RT, et al. Development of suitable interatomic potentials for simulation of liquid and amorphous Cu-Zr alloys. Philos Mag, 2009, 89: 967–987

    Article  CAS  Google Scholar 

  58. Yu P, Bai HY, Tang MB, et al. Excellent glass-forming ability in simple Cu50Zr50-based alloys. J Non-Crystalline Solids, 2005, 351: 1328–1332

    Article  CAS  Google Scholar 

  59. Zemp J, Celino M, Schönfeld B, et al. Crystal-like rearrangements of icosahedra in simulated copper-zirconium metallic glasses and their effect on mechanical properties. Phys Rev Lett, 2015, 115: 165501

    Article  CAS  Google Scholar 

  60. Ren N, Hu L, Wang B, et al. Structural topological signature of high-temperature non-arrhenius crossover in metallic glass-forming liquids. Scripta Mater, 2021, 200: 113926

    Article  CAS  Google Scholar 

  61. Wang B, Luo L, Guo E, et al. Nanometer-scale gradient atomic packing structure surrounding soft spots in metallic glasses. npj Comput Mater, 2018, 4: 41

    Article  CAS  Google Scholar 

  62. Şopu D, Scudino S, Bian XL, et al. Atomic-scale origin of shear band multiplication in heterogeneous metallic glasses. Scripta Mater, 2020, 178: 57–61

    Article  CAS  Google Scholar 

  63. Wakeda M, Shibutani Y, Ogata S, et al. Relationship between local geometrical factors and mechanical properties for Cu-Zr amorphous alloys. Intermetallics, 2007, 15: 139–144

    Article  CAS  Google Scholar 

  64. Foroughi A, Tavakoli R, Aashuri H. Molecular dynamics study of structural formation in Cu50-Zr50 bulk metallic glass. J Non-Crystalline Solids, 2016, 432: 334–341

    Article  CAS  Google Scholar 

  65. Yue X, Brechtl J, Wang F, et al. Deformation behavior of annealed Cu64Zr36 metallic glass via molecular dynamics simulations. Mater Des, 2020, 191: 108660

    Article  CAS  Google Scholar 

  66. Hirata A, Kang LJ, Fujita T, et al. Geometric frustration of icosahedron in metallic glasses. Science, 2013, 341: 376–379

    Article  CAS  Google Scholar 

  67. Hu YC, Tanaka H. Physical origin of glass formation from multi-component systems. Sci Adv, 2020, 6: eabd2928

    Article  CAS  Google Scholar 

  68. Cargill Iii GS, Spaepen F. Description of chemical ordering in amorphous alloys. J Non-Crystalline Solids, 1981, 43: 91–97

    Article  CAS  Google Scholar 

  69. Raikher YL, Rusakov VV, Perzynski R. Brownian motion in a viscoelastic medium modelled by a Jeffreys fluid. Soft Matter, 2013, 9: 10857–10865

    Article  CAS  Google Scholar 

  70. Raikher YL, Rusakov VV. Theory of Brownian motion in a Jeffreys fluid. J Exp Theor Phys, 2010, 111: 883–889

    Article  CAS  Google Scholar 

  71. Mason TG, Weitz DA. Optical measurements of frequency-dependent linear viscoelastic moduli of complex fluids. Phys Rev Lett, 1995, 74: 1250–1253

    Article  CAS  Google Scholar 

  72. Li B, Lou K, Kob W, et al. Anatomy of cage formation in a two-dimensional glass-forming liquid. Nature, 2020, 587: 225–229

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (52071217) and Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots.

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Authors and Affiliations

  1. College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, 518060, China

    Xiaodi Liu  (刘晓俤), Jinsen Tian  (田锦森), Jiang Ma  (马将) & Jun Shen  (沈军)

  2. Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen University, Shenzhen, 518060, China

    Xiaodi Liu  (刘晓俤) & Jun Shen  (沈军)

  3. Department of Mechanical Engineering, College of Engineering, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong SAR, China

    Quanfeng He  (赫全锋), Ziqing Zhou  (周子清) & Yong Yang  (杨勇)

  4. School of Materials Science and Engineering, Tongji University, Shanghai, 201804, China

    Wenfei Lu  (卢文飞)

  5. Shanghai Engineering Research Center of Physical Vapor Deposition (PVD) Superhard Coating and Equipment, Shanghai Institute of Technology, Shanghai, 201418, China

    Dandan Liang  (梁丹丹)

  6. Department of Materials Science and Engineering, College of Engineering, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong SAR, China

    Yong Yang  (杨勇)

Authors
  1. Xiaodi Liu  (刘晓俤)
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  2. Quanfeng He  (赫全锋)
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  3. Wenfei Lu  (卢文飞)
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  4. Ziqing Zhou  (周子清)
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  6. Dandan Liang  (梁丹丹)
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  7. Jiang Ma  (马将)
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  8. Yong Yang  (杨勇)
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  9. Jun Shen  (沈军)
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Contributions

Author contributions Liu X and Shen J conceived the project; Shen J supervised the project; Liu X and Lu W performed the MD simulations; Liu X and Zhou Z carried out the ML; Liu X, He Q and Tian J derived the equations; Liu X wrote the manuscript. All authors discussed the results and commented on the manuscript.

Corresponding author

Correspondence to Jun Shen  (沈军).

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Conflict of interest The authors declare that they have no conflict of interest.

Additional information

Xiaodi Liu obtained his bachelor and master’s degrees from Shandong University in 2012 and 2015, respectively. He received his doctoral degree from the City University of Hong Kong in 2018. He is currently a full-time associate research fellow at Shenzhen University. His research focuses on the mechanical and catalytic properties of metallic glasses.

Jun Shen worked at Harbin Institute of Technology from 1993 to 2011 and became a professor in 1999. During 2003 to 2005, he successively visited The University of Sydney and University of Nottingham. After working at Tongji University in 2011–2018, he joined Shenzhen University. His research focuses on the metallic glasses and light metal structural materials, including glass forming ability, mechanical behavior and mechanism, and their application to precision optical devices and electromobile motors.

Supplementary information Details of the modelling and computation and supporting data are available in the online version of the paper.

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40843_2021_1990_MOESM1_ESM.pdf

Machine learning atomic dynamics to unfold the origin of plasticity in metallic glasses: From thermo- to acousto-plastic flow

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Liu, X., He, Q., Lu, W. et al. Machine learning atomic dynamics to unfold the origin of plasticity in metallic glasses: From thermo- to acousto-plastic flow. Sci. China Mater. 65, 1952–1962 (2022). https://doi.org/10.1007/s40843-021-1990-2

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