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

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. 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.

Automated summary

By combining machine learning capabilities and in-situ transmission electron microscopy measurements, a new deformation sequence of dislocation gliding and pinning, a quiescent period, and sudden release of localized strain in aluminum-carbon nanotube composites has been revealed. A plastic deformation mechanism with three essential characteristics has been proposed, and the implications for plasticity carriers in crystalline media and amorphous solids have been discussed.