Abstract: One of the long-standing grand challenges in computational materials science and condensed matter theory is to be able to reliably and accurately solve for the quantum mechanical properties of general materials. This capability is necessary in many areas ranging from materials design to prediction and interpretation of novel quantum phenomena. Today while there are many successful approximate methods, we lack general and widely applicable methods able to provide a ground truth. This is a general problem, but particularly problematic for systems such as quantum materials where the results are particularly delicate to approximations made.

In this talk, I will describe recent advances in quantum Monte Carlo methods, which combined with the increased capabilities promised by exascale computing promise to bring us significantly closer to the goal of reliable materials predictions.