Optimization of the diffusion Monte Carlo nodal surface

Diffusion Monte Carlo (DMC) is a method for computing the Fermionic ground state that represents the wave function as a density of points called walkers, which converge to the ground state by moving in a stochastic way. The main limitation of DMC is that it requires a-priori knowledge of the sign of the wave function. The standard practice is to set this sign equal to that of a trial function, which is typically an approximate wave function produced by some other method. This fixed node approximation limits the accuracy of DMC to the accuracy of the method that produces the trial function. The work presented here explores a novel method that optimizes the location nodal surface from the walker distribution of DMC. The parameters that determine the location of the nodal surface are continually improved via gradient descent, to minimize the DMC energy.