Density Functional Theory and Machine Learning Description and Prediction of Oxygen Atom Chemisorption on Platinum Surfaces and Nanoparticles

Universal Predictive Power: Introducing the Electronic Structure Decomposition Approach for CO Adsorption and Activation on Al2O3-Supported Ru Nanoparticles

Accurate prediction of catalyst performance is crucial for designing materials with specific catalytic functions. While the density functional theory (DFT) method is widely used for its accuracy, modeling heterogeneous systems, especially supported transition metals, poses significant computational challenges. To address these challenges, we introduce the Electronic Structure Decomposition Approach (ESDA), a novel method that identifies specific density of states (DOS) areas responsible for adsorbate interaction and activation on the catalyst. As a case study, we investigate the influence of α-Al2O3(0001) as a support material on CO adsorption energy and the stretching frequency of the C-O bond on Ru nanoparticles (NPs). Using multiple linear regression analysis, ESDA models were trained with data from isolated Ru NPs and adjusted using supported NP sample data. The ESDA models accurately predict the CO adsorption energies and C-O vibrational frequencies, demonstrating strong linear correlations between predicted and DFT-calculated values with low errors across various adsorption sites for both isolated and supported Ru NPs. Beyond pinpointing the DOS areas responsible for CO adsorption and C-O bond activation, this study provides insights into manipulating these DOS areas to control CO activation, hence facilitating CO dissociation. Additionally, ESDA significantly accelerates the characterization and prediction of CO adsorption and activation on both isolated and supported Ru NPs compared to DFT calculations, expediting the design of new catalytic materials and advancing catalysis research. Furthermore, ESDA's reliance on the electronic structure as a descriptor suggests its potential for predicting various properties beyond catalysis, broadening its applicability across diverse scientific domains.

Machine Learning for Experimental Reactivity of a Set of Metal Clusters toward C-H Activation

Understanding the mechanisms of C-H activation of alkanes is a very important research topic. The reactions of metal clusters with alkanes have been extensively studied to reveal the electronic features governing C-H activation, while the experimental cluster reactivity was qualitatively interpreted case by case in the literature. Herein, we prepared and mass-selected over 100 rhodium-based clusters (RhxVyOz- and RhxCoyOz-) to react with light alkanes, enabling the determination of reaction rate constants spanning six orders of magnitude. A satisfactory model being able to quantitatively describe the rate data in terms of multiple cluster electronic features (average electron occupancy of valence s orbitals, the minimum natural charge on the metal atom, cluster polarizability, and energy gap involved in the agostic interaction) has been constructed through a machine learning approach. This study demonstrates that the general mechanisms governing the very important process of C-H activation by diverse metal centers can be discovered by interpreting experimental data with artificial intelligence.

Magnesium-Induced Strain and Immobilized Radical Generation on the Boron Oxide Surface Enhances the Oxidation Rate of Boron Particles: A DFTB-MD Study

Despite their high gravimetric and volumetric energy densities, boron (B) particles suffer from poor oxidative energy release rates as the boron oxide (B2O3) shell impedes the diffusivity of O2 to the particle interior. Recent experiemental studies have shown that the addition of metals with a lower free energy of oxidation, such as Mg, can reduce the oxide shell of B and enhance the energetic performance of B by ∼30-60%. However, the exact underlying mechanism behind the reactivity enhancement is unknown. Here, we performed DFTB-MD simulations to study the reaction of Mg vapor with a B2O3 surface. We found that the Mg becomes oxidized on the B2O3 surface, forming a MgBxOy phase, which induces a tensile strain in the B-O bond at the MgBxOy-B2O3 interface, simultaneously reducing the interfacial B and thereby developing dangling bonds. The interfacial bond straining creates an overall surface expansion, indicating the presence of a net tensile strain. The B with dangling bonds can act as active centers for gas-phase O2 adsorption, thereby increasing the adsorption rate, and the overall tensile strain on the surface will increase the diffusion flux of adsorbed O through the surface to the particle core. As the overall B particle oxidation rate is dependent on both the O adsorption and diffusion rates, the enhancement in both of these rates increases the overall reactivity of B particles.

Combustion-Assisted Polyol Reduction Method to Prepare Highly Transparent and Efficient Pt Counter Electrodes for Bifacial Dye-Sensitized Solar Cells

A combustion-assisted polyol reduction (CPR) method has been developed to deposit electrocatalytically efficient and transparent Pt counter electrodes (CEs) for bifacial dye-sensitized solar cells (DSSCs). Compared with conventional thermal decomposition of Pt precursors, CPR allows for a decrease in reduction temperature to 150 °C. The low-temperature processing is attributed to adding an organic fuel, acetylacetone (Hacac), which provides extra heat to lower reduction energy. In addition, the stable Pt complexes can simultaneously be formed in ethylene glycol (EG) and Hacac system, which leads to Pt nanoparticle size regulation. A ratio of Hacac to EG is optimized to achieve excellent electrocatalytic activity and high visible light transmittance for CEs. The bifacial DSSCs fabricated with CPR-Pt CEs (EG : Hacac=1 : 16) reach efficiencies of 6.71±0.16% and 6.41±0.15% in front and back irradiations, respectively.

Pt nanoparticles under oxidizing conditions - implications of particle size, adsorption sites and oxygen coverage on stability

Platinum nanoparticles are efficient catalysts for different reactions, such as oxidation of carbon and nitrogen monoxides. Adsorption and interaction of oxygen with the nanoparticle surface, taking place under reaction conditions, determine not only the catalytic efficiency but also the stability of the nanoparticles against oxidation. In this study, platinum nanoparticles in oxygen environment are investigated by systematic screening of initial nanoparticle-oxygen configurations and employing density functional theory and a thermodynamics-based approach. The structures formed at low oxygen coverages are described by adsorption of atomic oxygen on the nanoparticles whereas at high coverages oxide-like species are formed. The relative stability of adsorption configurations at different oxygen coverages, including the phase of fully oxidized nanoparticles, is investigated by constructing p-T phase diagrams for the studied systems.

Exploring Structure-Sensitive Relations for Small Species Adsorption Using Machine Learning

Accurate prediction of adsorption energies on heterogeneous catalyst surfaces is crucial to predicting reactivity and screening materials. Adsorption linear scaling relations have been developed extensively but often lack accuracy and apply to one adsorbate and a single binding site type at a time. These facts undermine their ability to predict structure sensitivity and optimal catalyst structure. Using machine learning on nearly 300 density functional theory calculations, we demonstrate that generalized coordination number scaling relations hold well for oxygen- and high-valency carbon-binding species but fail for others. We reveal that the valency and the electronic coupling of a species with the surface, along with the site type and its coordination environment, are critical for small species adsorption. The model simultaneously predicts the adsorption energy and preferred site and significantly outperforms linear scalings in accuracy. It can expose the structure sensitivity of chemical reactions and enable enhanced catalyst activity via engineering particle shape and facet defects. The generality of our methodology is validated by training the model with transition metal data and transferring it to predict adsorption energies on single-atom alloys.