Highly accurate potential energy surfaces with deep quantum Monte Carlo

The combination of deep learning with variational Monte Carlo offers a promising new direction to compute properties of quantum systems with high accuracy and acceptable computational complexity without having to specifically taylor the method to the specific molecule or material. Building on the PauliNet deep quantum Monte Carlo architecture we present new approaches and results on using fermionic networks to solve quantum chemical systems with high accuracy.