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