Neutron matter Variational Monte Carlo calculations with artificial neural network wave functions

We utilize the hidden-fermion family of neural network quantum states as the wave function ansatz for Variational Monte Carlo (VMC) calculations of periodic neutron matter. This same method has been used to accurately approximate wave functions for nuclei as large as ¹⁶O. The trained neural network wave functions have approximately the same energies predicted by previous Diffusion Monte Carlo (DMC) calculations. Using the same wave functions we compute singlet and triplet channel pair distribution functions and find evidence of pairing in the singlet channel at low densities, signaling the expected neutron superfluid. We will soon be able to apply these techniques to more realistic nuclear potentials to obtain even more accurate neutron matter wave functions.