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

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 defmite 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.

Automated summary

Metallic glasses have a non-uniform structure and dynamics at the nanoscale, with static and dynamic heterogeneities being crucial for their deformation mechanism. Machine learning can be used to learn defects in MGs from atomic trajectories and identify which atoms behave like liquids under stress.