Towards catalysis modeling with QMC

Predictive modeling of catalytic processes on surfaces is challenging because of large uncertainties in computed energetics using density functional theory (DFT), and exponential dependence of catalyst performance on energies. In particular, the use of various DFT functionals and approximations results in qualitatively different results. The problem is compounded when treating transition metal oxides where a host of DFT errors persist. In this work, made possible by leadership scale high performance computers, we use quantum Monte Carlo (QMC) to determine the adsorption energies of the CO molecule on Cu$_2$O (110) surface for various geometries determined by different DFT approximations. The relationships between geometry and energies, and between DFT and QMC results, will be discussed.