  We introduce a new family of trial wave-functions based on deep neural
networks to solve the many-electron Schr\"odinger equation. The Pauli exclusion
principle is dealt with explicitly to ensure that the trial wave-functions are
physical. The optimal trial wave-function is obtained through variational Monte
Carlo and the computational cost scales quadratically with the number of
electrons. The algorithm does not make use of any prior knowledge such as
atomic orbitals. Yet it is able to represent accurately the ground-states of
the tested systems, including He, H2, Be, B, LiH, and a chain of 10 hydrogen
atoms. This opens up new possibilities for solving large-scale many-electron
Schr\"odinger equation.
