  We develop a deep neural network (DNN) that accounts for the phase behaviors
of polymer-containing liquid mixtures. The key component in the DNN consists of
a theory-embedded layer that captures the characteristic features of the phase
behavior via coarse-grained mean-field theory and scaling laws and
substantially enhances the accuracy of the DNN. Moreover, this layer enables us
to reduce the size of the DNN for the phase diagrams of the mixtures. This
study also presents the predictive power of the DNN for the phase behaviors of
polymer solutions and salt-free and salt-doped diblock copolymer melts.
