  The study of alloys using computational methods has been a difficult task due
to the usually unknown stoichiometry and local atomic ordering of the different
structures experimentally. In order to combat this, first-principles methods
have been coupled with statistical methods such as the Cluster Expansion
formalism in order to construct the energy hull diagram, which helps to
determine if an alloyed structure can exist in nature. Traditionally, density
functional theory (DFT) has been used in such workflows. In this work we
propose to use chemically accurate many-body variational Monte Carlo (VMC) and
diffusion Monte Carlo (DMC) methods to construct the energy hull diagram of an
alloy system, due to the fact that such methods have a weaker dependence on the
starting wavefunction and density functional, scale similarly to DFT with the
number of electrons, and have had demonstrated success for a variety of
materials. To carry out these simulations in a high-throughput manner, we
propose a method called Jastrow sharing, which involves recycling the optimized
Jastrow parameters between alloys with different stoichiometries. We show that
this eliminates the need for extra VMC Jastrow optimization calculations and
results in a significant computational cost savings (on average 1/4 savings of
total computational time). Since it is a novel post-transition metal
chalcogenide alloy series that has been synthesized in its few-layer form, we
used monolayer $GaS_xSe_{1-x}$ as a case study for our workflow. By extensively
testing our Jastrow sharing procedure for monolayer $GaS_xSe_{1-x}$ and
quantifying the cost savings, we demonstrate how a pathway towards chemically
accurate high-throughput simulations of alloys can be achieved using many-body
VMC and DMC methods.
