  Data-driven approaches to inferring the local structures responsible for
plasticity in amorphous materials have made substantial contributions to our
understanding of the failure, flow, and rearrangement dynamics of supercooled
fluids. Some of these methods, such as the ``softness'' approach based on
linear support vector machines, have identified combinations of local
structural features of a supercooled particle's environment that predict energy
barriers associated with particle rearrangements. This approach also predicts
the onset temperature, often characterized as the temperature below which the
system's dynamics becomes non-Arrhenius and above which local structures are no
longer predictive of dynamical activity. We implement a transfer-learning
approach in which we first show that classifiers can be trained to predict
dynamical activity even far above the onset temperature. We then show that
applying these classifiers to data from the supercooled phase recovers
essentially the same physical information about the relationship between local
structures and energy barriers that softness does.
