Origin of copper as a unique catalyst for C-C coupling in electrocatalytic CO2 reduction.

High yields of C2 products through electrocatalytic CO2 reduction (eCO2R) can only be obtained using Cu-based catalysts. Here, we adopt the generalized frontier molecular orbital (MO) theory based on first-principles calculations to identify the origin of this unique property of Cu. We use the grand canonical ensemble (or fixed potential) approach to ensure that the calculated Fermi level, which serves as the frontier orbital of the metal catalyst, accurately represents the applied electrode potentials. We determine that the key intermediate OCCO assumes a U-shape configuration with the two C atoms bonded to the Cu substrate. We identify the frontier MOs that are involved in the C-C coupling. The good alignment of the Fermi level of Cu with these frontier MOs is perceived to account for the excellent catalytic performance of Cu for C-C coupling. It is expected that these new insights could provide useful guidance in tuning Cu-based catalysts as well as designing non-Cu catalysts toward high-efficiency eCO2R.

Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials.

Realistically modelling how solvents affect catalytic reactions is a longstanding challenge due to its prohibitive computational cost. Typically, an explicit atomistic treatment of the solvent molecules is needed together with molecular dynamics (MD) simulations and enhanced sampling methods. Here, we demonstrate the utility of machine learning interatomic potentials (MLIPs), coupled with active learning, to enable fast and accurate explicit solvent modelling of adsorption and reactions on heterogeneous catalysts. MLIPs trained on-the-fly were able to accelerate ab initio MD simulations by up to 4 orders of magnitude while reproducing with high fidelity the geometrical features of water in the bulk and at metal-water interfaces. Using these ML-accelerated simulations, we accurately predicted key catalytic quantities such as the adsorption energies of CO*, OH*, COH*, HCO*, and OCCHO* on Cu surfaces and the free energy barriers of C-H scission of ethylene glycol over Cu and Pd surfaces, as validated with ab initio calculations. We envision that such simulations will pave the way towards detailed and realistic studies of solvated catalysts at large time- and length-scales.

Computational Methods in Heterogeneous Catalysis.

The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. We finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.

Zeolite-Encaged Pd-Mn Nanocatalysts for CO2 Hydrogenation and Formic Acid Dehydrogenation.

A CO2 -mediated hydrogen storage energy cycle is a promising way to implement a hydrogen economy, but the exploration of efficient catalysts to achieve this process remains challenging. Herein, sub-nanometer Pd-Mn clusters were encaged within silicalite-1 (S-1) zeolites by a ligand-protected method under direct hydrothermal conditions. The obtained zeolite-encaged metallic nanocatalysts exhibited extraordinary catalytic activity and durability in both CO2 hydrogenation into formate and formic acid (FA) dehydrogenation back to CO2 and hydrogen. Thanks to the formation of ultrasmall metal clusters and the synergic effect of bimetallic components, the PdMn0.6 @S-1 catalyst afforded a formate generation rate of 2151 molformate molPd -1 h-1 at 353 K, and an initial turnover frequency of 6860 mol H 2 molPd -1 h-1 for CO-free FA decomposition at 333 K without any additive. Both values represent the top levels among state-of-the-art heterogeneous catalysts under similar conditions. This work demonstrates that zeolite-encaged metallic catalysts hold great promise to realize CO2 -mediated hydrogen energy cycles in the future that feature fast charge and release kinetics.