Atomic-Scale Mechanism of Platinum Catalyst Restructuring under a Pressure of Reactant Gas.

Heterogeneous catalysis is key for chemical transformations. Understanding how catalysts' active sites dynamically evolve at the atomic scale under reaction conditions is a prerequisite for accurately determining catalytic mechanisms and predictably developing catalysts. We combine in situ time-dependent scanning tunneling microscopy observations and machine-learning-accelerated first-principles atomistic simulations to uncover the mechanism of restructuring of Pt catalysts under a pressure of carbon monoxide (CO). We show that a high CO coverage at a Pt step edge triggers the formation of atomic protrusions of low-coordination Pt atoms, which then detach from the step edge to create sub-nano-islands on the terraces, where under-coordinated sites are stabilized by the CO adsorbates. The fast and accurate machine-learning potential is key to enabling the exploration of tens of thousands of configurations for the CO-covered restructuring catalyst. These studies open an avenue to achieve an atomic-scale understanding of the structural dynamics of more complex metal nanoparticle catalysts under reaction conditions.

Quantifying robustness of DFT predicted pathways and activity determining elementary steps for electrochemical reactions.

Density functional theory calculations are being routinely used to screen for new catalysts. Typically, this involves invoking scaling relations leading to the Sabatier-type volcano relationship for the catalytic activity, where each leg represents a unique potential determining an elementary step. The success of such screening efforts relies heavily not only on the prediction robustness of the activity determining step, but also on the choice of the descriptor. This becomes even more important as these methods are being applied to determine selectivity between a variety of possible reaction products. In this work, we develop a framework to quantify the confidence in the classification problem of identifying the potential determining step for material candidates and subsequently the pathway selectivity toward different reaction products. We define a quantity termed as the classification efficiency, which is a quantitative metric to rank descriptors on the basis of robustness of predictions for identifying selectivity toward different reaction products and the limiting step for the corresponding pathway. We demonstrate this approach for the reactions of oxygen reduction and oxygen evolution, and identify that ΔGOOH* is the optimal descriptor to classify between 2e- and 4e- oxygen reduction. We further show that ΔGOH* and ΔGOOH* have comparable performance in identifying the limiting step for 4e- oxygen reduction reaction. In the case of oxygen evolution, we study all possible 2 descriptor models and identify that {ΔGOOH*,ΔGO* } and {ΔGOH* ,ΔGO* } both are highly efficient at classifying between 2e- and 4e- water oxidation. The presented methodology can directly be applied to other multi-electron electrochemical reactions such as CO2 and N2 reduction for improved mechanistic insights.

CO organization at ambient pressure on stepped Pt surfaces: first principles modeling accelerated by neural networks.

Step and kink sites at Pt surfaces have crucial importance in catalysis. We employ a high dimensional neural network potential (HDNNP) trained using first-principles calculations to determine the adsorption structure of CO under ambient conditions (T = 300 K and P = 1 atm) on these surfaces. To thoroughly explore the potential energy surface (PES), we use a modified basin hopping method. We utilize the explored PES to identify the adsorbate structures and show that under the considered conditions several low free energy structures exist. Under the considered temperature and pressure conditions, the step edge (or kink) is totally occupied by on-top CO molecules. We show that the step structure and the structure of CO molecules on the step dictate the arrangement of CO molecules on the lower terrace. On surfaces with (111) steps, like Pt(553), CO forms quasi-hexagonal structures on the terrace with the top site preferred, with on average two top site CO for one multiply bonded CO, while in contrast surfaces with (100) steps, like Pt(557), present a majority of multiply bonded CO on their terrace. Short terraced surfaces, like Pt(643), with square (100) steps that are broken by kink sites constrain the CO arrangement parallel to the step edge. Overall, this effort provides detailed analysis on the influence of the step edge structure, kink sites, and terrace width on the organization of CO molecules on non-reconstructed stepped surfaces, yielding initial structures for understanding restructuring events driven by CO at high coverages and ambient pressure.

Maximal Predictability Approach for Identifying the Right Descriptors for Electrocatalytic Reactions.

Density functional theory (DFT) calculations are being routinely used to identify new material candidates that approach activity near fundamental limits imposed by thermodynamics or scaling relations. DFT calculations are associated with inherent uncertainty, which limits the ability to delineate materials (distinguishability) that possess high activity. Development of error-estimation capabilities in DFT has enabled uncertainty propagation through activity-prediction models. In this work, we demonstrate an approach to propagating uncertainty through thermodynamic activity models leading to a probability distribution of the computed activity and thereby its expectation value. A new metric, prediction efficiency, is defined, which provides a quantitative measure of the ability to distinguish activity of materials and can be used to identify the optimal descriptor(s) ΔGopt. We demonstrate the framework for four important electrochemical reactions: hydrogen evolution, chlorine evolution, oxygen reduction and oxygen evolution. Future studies could utilize expected activity and prediction efficiency to significantly improve the prediction accuracy of highly active material candidates.