Deep Quantum Monte Carlo Learning for the Direct Solution of the Electronic Schr"odinger Equation

Ab-initio quantum chemistry with neural-network wavefunctions

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.