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The elementary excitations in metallic glasses (MGs), i.e., $\beta$ processesthat involve hopping between nearby sub-basins, underlie many unusualproperties of the amorphous alloys. A high-efficacy prediction of thepropensity for those activated processes from solely the atomic positions,however, has remained a daunting challenge. Recently, employing well-designedsite environment descriptors and machine learning (ML), notable progress hasbeen made in predicting the propensity for stress-activated $\beta$ processes(i.e., shear transformations) from the static structure. However, the complextensorial stress field and direction-dependent activation would inducenon-trivial noises in the data, limiting the accuracy of the structure-propertymapping learned. Here, we focus on the thermally activated elementaryexcitations and generate high-quality data in several Cu-Zr MGs, allowingquantitative mapping of the potential energy landscape. After fingerprintingthe atomic environment with short- and medium-range interstice distribution, MLcan identify the atoms with strong resistance or high compliance to thermalactivation, at an unprecedented accuracy over ML models for stress-drivenactivation events. Interestingly, a quantitative "between-task" transferringtest reveals that our learnt model can also generalize to predict thepropensity of shear transformation. Our dataset is potentially useful forbenchmarking future ML models on structure-property relationships in MGs.

Predicting the propensity for thermally activated $\beta$ events in metallic glasses via interpretable machine learning