Understanding the influence of reaction conditions (O2 pressure and temperature) on metal-support interaction (MSI) is important to improve the thermal stability and catalytic reactivity of three-way catalyst (TWC). However, it is often difficult to reveal the stability and composition of TWC due to the dynamical change (oxidation, reduction, aggregation, etc) of metal species under catalytic working conditions. Computer simulation based on density functional theory (DFT) is a useful tool to investigate the MSI, but it is still challenging to predict accurate catalyst model due to the structural complexity. Herein, we elucidate the MSI of Pd/Sr3Ti2O7 (STO) under the TWC working conditions using DFT calculations combined with machine learning enhanced global optimization (GOFEE) and thermodynamics. Oxidation of several small Pd clusters is assessed by thermodynamics from a collection of global minimums of STO-supported PdxOysampling by advanced GOFEE searches. The square-planar PdO4 motif is identified as the elementary building block of oxidized clusters. Small metallic Pd clusters with formation of an oxygen vacancy at the substrate is predicted to be stable under the reductive atmosphere, while bulk-like PdO clusters are stable under stoichiometric and oxidative ones. We find that the MSI is further enhanced upon the oxidation of Pd clusters on both STO and γ-Al2O3 surfaces, indicating that the oxidative pretreatment will increase the thermal stability of the TWC. More importantly, compared with conventional γ-Al2O3, the interaction between small PdxOy clusters and STO (001) surfaces is much stronger, highlighting that the Pd/STO catalysts has higher sintering resistance. The growth of small oxidized PdxOy clusters is also studied, and some “magic” numbers in terms of stability against the Otsward ripening are found.

Metal-support interaction of Pd/Sr3Ti2O7 under the three-way catalyst atmosphere revealed by machine learning enhanced global optimization

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