The energy landscape framework offers a powerful tool to decipher the underlying physics governing the propensity for a glass to break in a brittle or ductile fashion. However, the relationship between the atomic structure of a glass and the topography of its energy landscape remains unclear. Here, we adopt the activation-relaxation technique (ART) to characterize the energy landscape of a model glass. Based on these results, we introduce a logistic regression model that successfully maps energy landscape to structure. This allows us to derive a structural metric that is correlated to the average height of the energy barriers that are locally accessible to the atoms in the network‚Äîwherein atoms associated with low-energy barriers are more likely to exhibit ductile reorganization under load.

Decoding the Energy Landscape and Nanoductility of Metallic Glasses by Machine Learning

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