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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.
GRAPHICAL ABSTRACT
IMPACT STATEMENT
A machine learning augmented investigation reveals the presence of cut-resistant nanotubes tilts the energy balance from crystal plasticity to more amorphous-like deformation mechanisms, particularly localized, transient strain observed in aluminum.
Acknowledgment
We acknowledge Ju Li for supporting this work, Alan Edelman for introducing us to Julia, Virginia Spanoudaki for sharing with us her expertise in data analysis through computer vision, Valentin Churavy for helping us to build the data analysis algorithm used in this work, Chris Rackauckas for guidance pertaining to scientific machine learning, and Viral Shah for sustained guidance to the Julia group at MIT. While the interpretations and discussions are our own, we have benefitted from conversations with Ting Zhu, Wei Cai, and Ken Kamrin. K.P. So was supported by the US DOE Office of Nuclear Energy's NEUP Program under Grant No. DE-NE0008827 and NSUF for facility access via RPA-18-14783. M.L. was partially supported by U.S. Department of Energy (DOE) Award No. DE-SC002194, National Science Foundation (NSF) DMR-2118448, and Norman C. Rasmussen Career Development Chair.
Disclosure statement
No potential conflict of interest was reported by the author(s).