Beyond the challenges posed by non-standardized data formats and different DFT methods, another critical issue that must be addressed is the potential inconsistencies between experimental and ab initio data. Understanding catalytic processes relies on microkinetic modeling based on DFT-derived energetics . However, the uncertainties in the energetics lead to discrepancies between theory and experiment, which impact the applicability and reliability of the multiscale model . Discrepancies can arise from various factors, such as incorrect active site models , missing pathways in the mechanism, inadequate consideration of surface environment under reaction conditions , and intrinsic errors in the DFT xc functional . DFT suffers from relatively large physical errors arising primarily from the xc approximation, which yields adsorption energies errors of ≈ 20 kJ mol -1 67 . While DFT has been instrumental in studying catalytic materials, intrinsic parametric errors translate to uncertainties in reaction rates of multiple orders of magnitude in multiscale models . Error cancellation improves the reliability of trends calcu-lated with DFT, but the errors are still too large for quantitative predictions of reaction rates for individual materials with firstprinciples-based kinetic models in most cases.