# Quantum Monte Carlo Benchmarking of Molecular Adsorption on Graphene-Supported Single Pt Atom

## TL;DR

This paper compares density functional theory and diffusion Monte Carlo methods to accurately predict molecular adsorption energetics on a single platinum atom supported by graphene, revealing significant differences with implications for catalysis design.

## Contribution

It provides a benchmark of DFT against DMC for adsorption properties on graphene-supported Pt, highlighting the importance of advanced methods for accurate catalytic predictions.

## Key findings

- DMC predicts different adsorption energetics than DFT.
- DFT shows different lowest-energy configurations and spin states.
- Large disparity in O2 and CO adsorption energies indicates CO poisoning risk.

## Abstract

The precise understanding of adsorption energetics and molecular geometry at catalytic sites is fundamental for advancing catalysis, particularly under the constraints of resource efficiency and environmental sustainability. This study benchmarks the performance of density functional theory (DFT) calculations against diffusion Monte Carlo (DMC) calculations for adsorption properties of small gas molecules relevant to CO oxidation -- namely O$_2$, CO, CO$_2$, and atomic oxygen -- on a single Pt atom supported by pristine graphene. Our findings reveal that DMC calculations provide a significantly different landscape of adsorption energetics compared to DFT results. Notably, DFT predicts different lowest-energy configurations and spin states, particularly for O$_2$, which suggests potential discrepancies in predicting the catalytic behavior. Furthermore, this study identifies the critical issue of CO poisoning, highlighted by the large disparity between the DMC adsorption energies of O$_2$ ($-1.23(2)$ eV) and CO ($-3.37(1)$ eV), which can inhibit the catalytic process. These results emphasize the necessity for more sophisticated computational approaches in catalysis research, aiming to refine the prediction accuracy of reaction mechanisms and to enhance the design of more effective catalysts.