ABSTRACT
Accurate theoretical simulation of electrochemical activation barriers is key to understanding electrocatalysis and guides the design of more efficient catalysts. Providing a detailed picture of proton transfer processes encounters several challenges: the constant potential requirement during charge transfer, the different time scales involved in the processes, and the thermal fluctuation of the solvent. Hence, it is prohibitively expensive computationally to apply density functional theory (DFT) calculations in modeling the potential-dependent activation barrier at the electrode-solvent interface, and the results are dubious. To address these challenges, we have developed an analytical approach based on charge conservation and decoupled potential energy surfaces to compute charge transfer barriers. The method makes it possible to simulate an electrochemical process at different potentials and explicitly include thermal fluctuations of the solvent at the electrode-solvent interface. We use the Pt-catalyzed alkaline hydrogen evolution reaction (HER) as our benchmark reaction, and we model the microkinetics of HER with consideration of the spatial fluctuations between the metal surface and the first solvent layer at room temperature. The distribution of water-metal distances has a large effect on the barriers of the charge transfer processes, and an accurate account of the statistical fluctuation in the reaction network leads to a several orders of magnitude increase in HER current as compared to transfer from a static solvent. The trends of the different reaction mechanisms in HER were successfully simulated with our model, and the theoretical I-V curves obtained are in good qualitative agreement with experimental results.
ABSTRACT
The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for example, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3-55 atoms with mean absolute errors in the range of 0.11-0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure-property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal-metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3-55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.
ABSTRACT
Understanding the mechanism behind the superior catalytic power of single- or few-atom heterogeneous catalysts has become an important topic in surface chemistry. This is particularly the case for gold, with TiO2 being an efficient support. Here we use scanning tunneling microscopy/spectroscopy with theoretical calculations to investigate the adsorption geometry and local electronic structure of several-atom Au clusters on rutile TiO2(110), with the clusters fabricated by controlled manipulation of single atoms. Our study confirms that Au1 and Au2 clusters prefer adsorption at surface O vacancies. Au3 clusters adsorb at O vacancies in a linear-chain configuration parallel to the surface; in the absence of O vacancies they adsorb at Ti5c sites with a structure of a vertically pointing upright triangle. We find that both the electronic structure and cluster-substrate charge transfer depend critically on the cluster size, bonding configuration, and local environment. This suggests the possibility of engineering cluster selectivity for specific catalytic reactions.
