Advanced search
Publication Cover
Materials Research Letters Volume 10, 2022 - Issue 1
Submit an article Journal homepage
1,748
Views
1
CrossRef citations to date
0
Altmetric
Original Reports

Observation of dynamical transformation plasticity in metallic nanocomposites through a precompiled machine-learning algorithm

Kang Pyo Soa Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USACorrespondence[email protected]
,
Myles Stapelberga Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
,
Yu Ren Zhoub Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
,
Mingda Lia Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
,
Michael P. Shorta Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
&
Sidney Yipa Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA;b Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
Pages 14-20 | Received 09 Aug 2021, Published online: 08 Jan 2022

References

  • So KP, Liu X, Mori H, et al. Ton-scale metal–carbon nanotube composite: The mechanism of strengthening while retaining tensile ductility. Extreme Mech Lett. 2016;8:245–250.
  • Spaepen, F. A microscopic mechanism for steady state inhomogeneous flow in metallic glasses. Acta Metall. 1977;25(4):407–415.
  • Argon A. Plastic deformation in metallic glasses. Acta Metall. 1979;27(1):47–58.
  • Falk ML, Langer JS. Dynamics of viscoplastic deformation in amorphous solids. Phys Rev E. 1998;57(6):7192.
  • Fessler JA. Optimization methods for magnetic resonance image reconstruction: Key models and optimization algorithms. IEEE Signal Process Mag. 2020;37(1):33–40.
  • Ravishankar S, Ye JC, Fessler JA. Image reconstruction: from sparsity to data-adaptive methods and machine learning. Proc IEEE. 2019;108(1):86–109.
  • Bezanson J, Karpinski S, Shah VB, et al. Julia: A fast dynamic language for technical computing. arXiv. 2012: 1209.5145.
  • Perkel JM. Julia: come for the syntax, stay for the speed. Nature. 2019;572(7768):141–143.
  • Bezanson J, Edelman A, Karpinski S, et al. Julia: A fresh approach to numerical computing. SIAM Rev. 2017;59(1):65–98.
  • Cao P, Short MP, Yip S. Potential energy landscape activations governing plastic flows in glass rheology. Proc Natl Acad Sci USA. 2019;116(38):18790–18797.
  • Poirier J. On transformation plasticity. J Geophys Res: Solid Earth. 1982;87(B8):6791–6797.
  • Julia code and TEM data repository. (2021). https://www.dropbox.com/sh/6q0j45dw8abjyee/AACS0QkWcOrLWABCoYEFpAQwa?dl=0.
  • Fessler JA. Michigan image reconstruction toolbox. Ann Arbor (MI): Jeffrey Fessler; 2018. https://web.eecs.umich.edu/∼fessler/code.
  • Danisch S, Krumbiegel J. Makie. jl: flexible high-performance data visualization for julia. J Open Source Softw. 2021;6(65):3349.
  • Ledig C, Theis L, Huszár F, et al., editors. Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE conference on computer vision and pattern recognition; 2017.
  • Dong C, Loy CC, Tang X. Accelerating the super-resolution convolutional neural network. In: European conference on computer vision. Cham: Springer; 2016, October. pp. 391–407.
  • Kim J, Lee JK, Lee KM. editors. Accurate image super-resolution using very deep convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition; 2016.
  • Lim B, Son S, Kim H, et al. editors. Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE conference on computer vision and pattern recognition workshops; 2017.
  • Wen B, Li Y, Pfister L, et al. editors. Joint adaptive sparsity and low-rankness on the fly: an online tensor reconstruction scheme for video denoising. Proceedings of the IEEE international conference on computer vision; 2017.
  • Donoho DL. Compressed sensing. IEEE Trans Inf Theory. 2006;52(4):1289–1306.
  • Eldar YC, Kutyniok G. Compressed sensing: theory and applications. Cambridge university press; 2012.
  • Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Adv Neural Inf Process Syst. 2014;27:2672.
  • Poirier J. Creep of crystals. X: Cambridge University Press; Ch. 8; 2009.
  • Schuh CA, Hufnagel TC, Ramamurty U. Mechanical behavior of amorphous alloys. Acta Mater. 2007;55(12):4067–4109.
  • Demkowicz MJ, Argon AS. High-density liquid-like component facilitates plastic flow in a model amorphous silicon system. MRS Online Proceedings Library (OPL). 2003: 806.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.