Towards accurate prediction of catalytic activity in IrO2 nanoclusters via first principles-based variable charge force field

IrO2 is one of the most efficient electrocatalysts for the oxygen evolution reaction (OER), and also has other applications such as in pH sensors. Atomistic modeling of IrO2 is critical for understanding the structure, chemistry, and nanoscale dynamics of IrO2 in these applications. Such modeling has remained elusive due to the lack of an empirical force field (EFF) for IrO2. We introduce a first-principles-based EFF that couples the Morse (MS) potential with a variable charge equilibration method, QEq. The EFF parameters are optimized using a genetic algorithm (GA) on a density functional theory (DFT)-based training set. The resultant Morse plus QEq EFF, "MS-Q" in short, successfully reproduces the lattice parameters, elastic constants, binding energies, and internal coordinates of various polymorphs of IrO2 from DFT calculations. More importantly, MS-Q accurately captures key metrics for evaluating structural and chemical properties of catalysts such as surface energetics, equilibrium shape, electrostatic charges, oxygen vacancy formation energies, relative stability of low index rutile IrO2 surfaces, and pressure-induced phase transformations. The MS-Q EFF is used to predict the oxygen binding energy (E-ad), a well-known descriptor for OER activity, on various sites of a nanocatalyst. We find E-ad to be more favorable at low coordination sites, i.e. edges and corners, compared to planar facets; E-ad is also correlated with charge transfer between the adsorbed O and nanocrystal, highlighting the importance of variable charge electrostatics in modeling catalysis on metal oxide surfaces. Our variable charge force field offers encouraging prospects for carrying out large-scale reactive simulations to evaluate catalytic performance of IrO2 surfaces and nanostructures.