  We introduce GlassMLP, a machine learning framework using physics-inspired
structural input to predict the long-time dynamics in deeply supercooled
liquids. We apply this deep neural network to atomistic models in 2D and 3D.
Its performance is better than the state of the art while being more
parsimonious in terms of training data and fitting parameters. GlassMLP
quantitatively predicts four-point dynamic correlations and the geometry of
dynamic heterogeneity. Transferability across system sizes allows us to
efficiently probe the temperature evolution of spatial dynamic correlations,
revealing a profound change with temperature in the geometry of rearranging
regions.
