Assessing orbital optimization in variational and diffusion Monte Carlo

In this work, we investigate whether the use of orbital optimization in variational Monte Carlo is able to systematically improve diffusion Monte Carlo results on correlated magnetic systems, using CrSBr as a model system. We show that short-range Jastrow factors are important for improving diffusion Monte Carlo, regardless of the quality of the orbitals. Large active spaces are required to converge the variational energy, but ultimately orbital optimization produces worse diffusion Monte Carlo energies when compared to standard orbitals from density functional theory. We show that this increased bias is due to larger locality errors from the use of pseudopotentials, while the fixed-node error is actually improved by using orbital optimization. In addition, for observables other than energy, orbital optimization produces a systematically smaller mixed-estimator bias. Ultimately, we believe that orbital optimization provides a reliable method for improving variational and pure fixed-node energies, as well as reducing mixed-estimator bias.