  We introduce a machine-learning approach to predict the complex non-Markovian
dynamics of supercooled liquids from static averaged quantities. Compared to
techniques based on particle propensity, our method is built upon a theoretical
framework that uses as input and output system-averaged quantities, thus being
easier to apply in an experimental context where particle resolved information
is not available. In this work, we train a deep neural network to predict the
self intermediate scattering function of binary mixtures using their static
structure factor as input. While its performance is excellent for the
temperature range of the training data, the model also retains some
transferability in making decent predictions at temperatures lower than the
ones it was trained for, or when we use it for similar systems. We also develop
an evolutionary strategy that is able to construct a realistic memory function
underlying the observed non-Markovian dynamics. This method lets us conclude
that the memory function of supercooled liquids can be effectively
parameterized as the sum of two stretched exponentials, which physically
corresponds to two dominant relaxation modes.
