  Here we introduce a variation of the trap model of glasses based on softness,
a local structural variable identified by machine learning, in supercooled
liquids. Softness is a particle-based quantity that reflects the local
structural environment of a particle and characterizes the energy barrier for
the particle to rearrange. As in the trap model, we treat each particle's
softness, and hence energy barrier, as evolving independently. We show that
such a model reproduces many qualitative features of softness, and therefore
makes qualitatively reasonable predictions of behaviors such as the dependence
of fragility on density in a model supercooled liquid. We also show failures of
this simple model, indicating features of the dynamics of softness that may
only be explained by correlations.
