The marriage of density functional theory (DFT) and deep-learning methods has the potential to revolutionize modern computational materials science. Here we develop a deep neural network approach represent DFT Hamiltonian (DeepH) crystalline materials, aiming bypass computationally demanding self-consistent field iterations substantially improve efficiency ab initio electronic-structure calcula...