  Deep learning techniques have opened a new venue for electronic structure
theory in recent years. In contrast to traditional methods, deep neural
networks provide much more expressive and flexible wave function ansatz,
resulting in better accuracy and time scaling behavior. In order to study
larger systems while retaining sufficient accuracy, we integrate a powerful
neural-network based model (FermiNet) with the effective core potential method,
which helps to reduce the complexity of the problem by replacing inner core
electrons with additional semi-local potential terms in Hamiltonian. In this
work, we calculate the ground state energy of 3d transition metal atoms and
their monoxide which are quite challenging for original FermiNet work, and the
results are in good consistency with both experimental data and other
state-of-the-art computational methods. Our development is an important step
for a broader application of deep learning in the electronic structure
calculation of molecules and materials.
