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Accuracy of Density Functional Theory for Predicting Kinetics of Methanol Synthesis from CO and CO2 Hydrogenation on Copper

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journal contribution
posted on 13.07.2018, 00:00 by Maliheh Shaban Tameh, Albert K. Dearden, Chen Huang
Density functional theory (DFT) is widely used for investigating heterogeneous catalysis; however, the predictive power of DFT is determined by the approximation used in the exchange–correlation (XC) functionals. In this work, we systematically investigate how the kinetics of methanol synthesis predicted by DFT depends on the choice of XC functionals. Microkinetic modeling is performed based on the Gibbs energies calculated with XC functionals that represent three levels of accuracy: Perdew–Burke–Ernzerhof (PBE) functional, Heyd–Scuseria–Ernzerhof (HSE) hybrid functional, and the random phase approximation (RPA) functional. We show that the predicted kinetics strongly depends on the choice of XC functionals. Methanol’s turnover frequencies predicted by PBE and HSE are about 30 times faster than the predictions from RPA. PBE predicts that the overall barrier of CO hydrogenation is 0.56 eV lower than that of CO2 hydrogenation, therefore suggesting CO as the carbon source for methanol synthesis on copper. This contradicts previous isotope-labeling experiments that supported CO2 as the carbon source in industrial methanol synthesis; therefore, PBE suggests that metallic copper cannot be the active site for CO2 hydrogenation. On the other hand, the overall barrier of CO hydrogenation, predicted by HSE and RPA, is lower than the overall barrier of CO2 hydrogenation by 0.22 and 0.14 eV, respectively. This suggests that CO2 hydrogenation is also competitive for methanol production, and we cannot completely rule out the possibility that metallic copper is the active site for catalyzing CO2 hydrogenation. In addition, the prediction of the dominating adsorbates also strongly depends on the choice of XC functionals. Our results show that different XC functionals can predict different kinetics for methanol synthesis, which calls attention to the accuracy of DFT for modeling methanol synthesis.