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Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, the development of new coating systems is hindered by the large design space and difficulty in predicting the properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs. In this work, we use machine learning to train a deep neural network potential (DNP) for Y2Si2O7, which is then applied to calculate the thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict the phonon-based thermodynamic properties of Y2Si2O7 with good agreement to DFT and experiments. We also utilize the DNP to calculate the anisotropic, lattice direction-dependent coefficients of thermal expansion (CTEs) for Y2Si2O7. Molecular dynamics trajectories using the DNP correctly demonstrate the accurate prediction of the anisotropy of the CTE in good agreement with the diffraction experiments. In the future, this DNP could be applied to accelerate additional property calculations for Y2Si2O7 compared to DFT or experiments. 

Abstract Copper-based catalyst is uniquely positioned to catalyze the hydrocarbon formations through electrochemical CO₂reduction. The catalyst design freedom is limited for alloying copper with H-affinitive elements represented by platinum group metals because the latter would easily drive the hydrogen evolution reaction to override CO₂reduction. We report an adept design of anchoring atomically dispersed platinum group metal species on both polycrystalline and shape-controlled Cu catalysts, which now promote targeted CO₂reduction reaction while frustrating the undesired hydrogen evolution reaction. Notably, alloys with similar metal formulations but comprising small platinum or palladium clusters would fail this objective. With an appreciable amount of CO-Pd₁moieties on copper surfaces, facile CO^*hydrogenation to CHO^*or CO-CHO^*coupling is now viable as one of the main pathways on Cu(111) or Cu(100) to selectively produce CH₄or C₂H₄through Pd-Cu dual-site pathways. The work broadens copper alloying choices for CO₂reduction in aqueous phases. 

Water influences catalytic reactions in multiple ways, including energetic and mechanistic effects. While simulations have provided significant insight into the roles that H 2 O molecules play in aqueous-phase heterogeneous catalysis, questions still remain as to the extent to which H 2 O structures influence catalytic mechanisms. Specifically, influences of the configurational variability in the water structures at the catalyst interface are yet to be understood. Configurational variability is challenging to capture, as it requires multiscale approaches. Herein, we apply a multiscale sampling approach to calculate reaction thermodynamics and kinetics for COH* dehydrogenation to CO* and CH 3 OH* dehydrogenation to CH 2 OH* on Pt(111) catalysts under liquid H 2 O. We explore various pathways for these dehydrogenation reactions that could influence the overall mechanism of methanol decomposition by including participation of H 2 O structures both energetically and mechanistically. We find that the liquid H 2 O environment significantly influences the mechanism of COH* dehydrogenation to CO* but leaves the mechanism of CH 3 OH* dehydrogenation to CH 2 OH* largely unaltered. 

Aqueous phase reforming (APR) of sugar alcohol molecules derived from biomass, e.g. , C x H (2x+2) O x (aq) + x H 2 O → x CO 2 (g) + (2 x + 1)H 2 (g), creates hydrogen gas sustainably, making it an important component of future bio-refineries; however, problems with the cost, activity, and selectivity of present precious metal based catalysts impede its broader adoption. Ideally, new catalysts would be designed to optimize activity and selectivity; however, a comprehensive understanding of the APR mechanism is lacking. This is complicated by the fact that the primary biomass-derived sugar alcohols are large molecules (meaning that their reaction networks are large) and because of the presence of liquid water. Water influences catalytic phenomena in multiple ways, including altering the thermodynamics of catalytic surface species and participating in catalytic reactions. Understanding the mechanism of APR requires understanding these various effects; however, computational strategies based solely on density functional theory (DFT) are computationally prohibitive for such large and complicated reaction networks. In this work, we investigate the mechanism of APR reactions in the context of glycerol reforming. To calculate the reaction network, we combine DFT calculations, force-field molecular dynamics (MD) simulations, linear scaling relations (LSRs), transition state scaling (TSS) relationships, and data from the literature into a microkinetic model. The microkinetic model is run under vacuum and aqueous phases in order to learn about the roles of water molecules on the mechanism of glycerol APR. We identify four such roles: providing surface hydroxyl groups, which promote oxidation of surface CO formed in glycerol decomposition; promoting C–H scissions; promoting O–H scissions; and inhibiting the thermodynamics of decarbonylation of C3 intermediates.