(159b) Modeling and Design of Single-Atom Alloy Catalysts for CO2 Hydrogenation Reactions

Recently developed machine learning (ML) methods hold great promise for simultaneously reducing the computational cost and increasing the accuracy in catalysis modeling [1]. In this contribution we will discuss recent work aimed at predicting adsorption configurations and energies of complex molecules at the surfaces of transition metals and alloys using a graph-based Gaussian Process Regression model (WWL-GPR [2]). We apply the methodology to study CO₂ hydrogenation (reverse water-gas shift) over single-atom alloy (SAA) catalysts, i.e., diluted bimetallic materials made of a single metal atom dispersed within the surface layer of another metal. SAAs have recently attracted considerable interest since adsorption at the sites offered by their surfaces can break the scaling relationships between adsorption energies and reaction barriers that limit conventional catalysts [3]. We target a wide combinatorial space of elements of the periodic table, which makes a direct study with density-functional theory (DFT) computationally prohibitive. Hence, we produce a database of DFT-calculated energies on a limited number of materials (metals and SAAs) and apply ML techniques for the extrapolation to a much wider range of materials. We include in the study multiple crystal facets that catalyst nanoparticles can expose under reaction conditions. We use the WWL-GPR model to calculate adsorption energies and simpler models (e.g., Brønsted-Evans-Polanyi relations and gradient boosting models with feature vector input) to estimate the activation energies of the new materials. We employ mean-field microkinetic modeling to simulate the reaction kinetics, accounting for the contribution of the different active sites of the catalysts. Then, we apply sensitivity analysis and uncertainty quantification to identify the parameters that can improve the model predictions, and we refine them with additional DFT calculations. The application of the framework to CO₂ hydrogenation reactions (e.g., reverse water-gas shift) allows us to rationalize how reaction mechanisms and catalytic performances (i.e., activity and selectivity) change with the catalyst composition, paving the way toward the design and nano-engineering of SAA catalysts

References

[1] M. Andersen and K. Reuter, Acc. Chem. Res. 54, 2741 (2021).

[2] W. Xu, K. Reuter, and M. Andersen, Nat. Comp. Sci. 2, 443 (2022).

[3] RT. Hannagan et al., Chem. Rev. 120, 12044 (2020).