  Machine learning and specifically deep-learning methods have outperformed
human capabilities in many pattern recognition and data processing problems, in
game playing, and now also play an increasingly important role in scientific
discovery. A key application of machine learning in the molecular sciences is
to learn potential energy surfaces or force fields from ab-initio solutions of
the electronic Schr\"odinger equation using datasets obtained with density
functional theory, coupled cluster, or other quantum chemistry methods. Here we
review a recent and complementary approach: using machine learning to aid the
direct solution of quantum chemistry problems from first principles.
Specifically, we focus on quantum Monte Carlo (QMC) methods that use neural
network ansatz functions in order to solve the electronic Schr\"odinger
equation, both in first and second quantization, computing ground and excited
states, and generalizing over multiple nuclear configurations. Compared to
existing quantum chemistry methods, these new deep QMC methods have the
potential to generate highly accurate solutions of the Schr\"odinger equation
at relatively modest computational cost.
