(662b) Intermetallic Catalyst Discovery for Selective Hydrogenation Reactions

Designing selective heterogeneous catalysts with precise control over catalytic site ensembles is a key goal in catalysis research. Intermetallic compounds offer a promising avenue due to their precise local atomic composition and structure, which enables systematic manipulation of the arrangement of multiple metals in an active site. In this study, we present a computational workflow, integrated with experimental collaboration, to accelerate the discovery of intermetallic catalysts for selective hydrogenation reactions.

We focus on the combination of inert hosts with active late transition metals and employ a range of computational approaches to identify intermetallic bulk and surface structures, automate adsorbate placement, and screen for surfaces with promising adsorbate/intermediate binding energies. Additionally, we introduce an automated transition state approach that enables us to identify and characterize the reaction mechanism for selective hydrogenation reactions.

After rapidly detecting the most stable bulk structures over a composition space, all low-index facets of these bulk structures are enumerated to determine their active site nuclearities. Complex key adsorbates are placed on these low-index facets using an automated placement approach, generating a database of DFT adsorption energies. This database is used as a training set for machine learning approaches, allowing for the rapid expansion of adsorption energies to identify surfaces with targeted binding properties.

Furthermore, we employ an automated transition state approach that enables the identification of the reaction mechanism for selective hydrogenation reactions on intermetallic surfaces. By analyzing the transition state structures, we can gain insight into the reaction pathway and identify active sites with optimal selectivity.

Our computational workflow, shown in Figure 1, integrated with experimental collaboration and automated transition state analysis, provides a rapid and efficient approach for identifying intermetallic catalysts for selective hydrogenation reactions. This approach has the potential to significantly accelerate the discovery of new and efficient heterogeneous catalysts.