Jane Kim (MSU/NSCL)
Neural Network Ansätze for Infinite Matter
 
Artificial neural networks have shown tremendous promise as a flexible ansatz for quantum many-body problems. In this work, we approximately solve the Schroedinger equation by performing variational Monte Carlo calculations with a deep, permutation-invariant neural network as a Jastrow correlator. We discuss the reinforcement learning scheme and the stochastic reconfiguration algorithm which helps stabilize the optimization of the wave function parameters. Ground state energies for the three-dimensional electron gas and infinite neutron matter will be compared to standard variational and diffusion Monte Carlo results. 
 
This work is supported by the U.S. National Science Foundation under grants No. PHY-1404159 and PHY-2013047.