  We introduce the Deep Post-Hartree-Fock (DeePHF) method, a machine learning
based scheme for constructing accurate and transferable models for the
ground-state energy of electronic structure problems. DeePHF predicts the
energy difference between results of highly accurate models such as the coupled
cluster method and low accuracy models such as the the Hartree-Fock (HF)
method, using the ground-state electronic orbitals as the input. It preserves
all the symmetries of the original high accuracy model. The added computational
cost is less than that of the reference HF or DFT and scales linearly with
respect to system size. We examine the performance of DeePHF on organic
molecular systems using publicly available datasets and obtain the state-of-art
performance, particularly on large datasets.
