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

Kang Pyo So; Myles Stapelberg; Yu Ren Zhou; Mingda Li; Michael P. Short; Sidney Yip

doi:10.1080/21663831.2021.2005700

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

Original Reports | 更新时间：2026-08-19

- Observation of dynamical transformation plasticity in metallic nanocomposites through a precompiled machine-learning algorithm
- Observation of dynamical transformation plasticity in metallic nanocomposites through a precompiled machine-learning algorithm
- 材料研究快报（英文） 2022年10卷第1期页码：14-20
- 作者机构：
  
  1. a Department of Nuclear Science and Engineering Massachusetts Institute of Technology Cambridge
  2. a Department of Nuclear Science and Engineering Massachusetts Institute of Technology Cambridge MA USA
  3. b Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge
- 作者简介：
- 基金信息：
- DOI：10.1080/21663831.2021.2005700
  中图分类号：
- 网络首发：2022-01-08，
  
  纸质出版：2022
- 稿件说明：
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Kang Pyo So, Myles Stapelberg, Yu Ren Zhou, 等. Observation of dynamical transformation plasticity in metallic nanocomposites through a precompiled machine-learning algorithm[J]. 材料研究快报（英文）, 2022,10(1):14-20.

Kang Pyo So, Myles Stapelberg, Yu Ren Zhou, et al. Observation of dynamical transformation plasticity in metallic nanocomposites through a precompiled machine-learning algorithm[J]. Materials Research Letters, 2022, 10(1): 14-20.
Kang Pyo So, Myles Stapelberg, Yu Ren Zhou, 等. Observation of dynamical transformation plasticity in metallic nanocomposites through a precompiled machine-learning algorithm[J]. 材料研究快报（英文）, 2022,10(1):14-20. DOI： 10.1080/21663831.2021.2005700.

Kang Pyo So, Myles Stapelberg, Yu Ren Zhou, et al. Observation of dynamical transformation plasticity in metallic nanocomposites through a precompiled machine-learning algorithm[J]. Materials Research Letters, 2022, 10(1): 14-20. DOI： 10.1080/21663831.2021.2005700.

摘要

Abstract

Machine learning capabilities combined with

in-situ

TEM measurements on aluminum-carbon nanotube composites reveal a new deformation sequence of dislocation gliding and pinning

a quiescent period

and finally a sudden release of localized strain. We propose a plastic deformation mechanism operating with three essential distinguishing characteristics: correlation of spatially localized microstrustural defects on the scale of nanometers

barrier-activation process of shear stress loading giving rise to strain response

and transient response on the time scale of seconds. Implications regarding plasticity carriers known to operate in crystalline media and in amorphous solids such as metallic glasses are discussed.

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相关机构

a Singapore Institute of Manufacturing Technology A*STAR

b Department of Materials Science & Engineering Southern University of Science and Technology

State Key Laboratory of Automotive Simulation and Control Jilin University Changchun People’s Republic of China

Key Laboratory of Automobile Materials Ministry of Education and School of Materials Science and Engineering Jilin University

State Key Laboratory of Nonlinear Mechanics Institute of Mechanics Chinese Academy of Sciences Beijing People’s Republic of China

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