Theoretical Prediction of Thermal Expansion Anisotropy for Y2Si2O7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment

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.

Dual-site catalysts featuring platinum-group-metal atoms on copper shapes boost hydrocarbon formations in electrocatalytic CO2 reduction

Copper-based catalyst is uniquely positioned to catalyze the hydrocarbon formations through electrochemical CO₂ reduction. 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 CO₂ reduction. 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 CO₂ reduction 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-Pd₁ moieties 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 CH₄ or C₂H₄ through Pd-Cu dual-site pathways. The work broadens copper alloying choices for CO₂ reduction in aqueous phases.

Insights into how the aqueous environment influences the kinetics and mechanisms of heterogeneously-catalyzed COH* and CH3OH* dehydrogenation reactions on Pt(111)

Water influences catalytic reactions in multiple ways, including energetic and mechanistic effects. While simulations have provided significant insight into the roles that H2O molecules play in aqueous-phase heterogeneous catalysis, questions still remain as to the extent to which H2O 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 CH3OH* dehydrogenation to CH2OH* on Pt(111) catalysts under liquid H2O. We explore various pathways for these dehydrogenation reactions that could influence the overall mechanism of methanol decomposition by including participation of H2O structures both energetically and mechanistically. We find that the liquid H2O environment significantly influences the mechanism of COH* dehydrogenation to CO* but leaves the mechanism of CH3OH* dehydrogenation to CH2OH* largely unaltered.

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

A significant number of heterogeneously-catalyzed chemical processes occur under liquid conditions, but simulating catalyst function under such conditions is challenging when it is necessary to include the solvent molecules. The bond breaking and forming processes modeled in these systems necessitate the use of quantum chemical methods. Since molecules in the liquid phase are under constant thermal motion, simulations must also include configurational sampling. This means that multiple configurations of liquid molecules must be simulated for each catalytic species of interest. The goal of the protocol presented here is to generate and sample trajectories of configurations of liquid water molecules around catalytic species on flat transition metal surfaces in a way that balances chemical accuracy with computational expense. Specifically, force field molecular dynamics (FFMD) simulations are used to generate configurations of liquid molecules that can subsequently be used in quantum mechanics-based methods such as density functional theory or ab initio molecular dynamics. To illustrate this, in this manuscript, the protocol is used for catalytic intermediates that could be involved in the pathway for the decomposition of glycerol (C3H8O3). The structures that are generated using FFMD are modeled in DFT in order to estimate the enthalpies of solvation of the catalytic species and to identify how H2O molecules participate in catalytic decompositions.

Insights into the roles of water on the aqueous phase reforming of glycerol

Microkinetic modeling using energies from DFT and scaling relations to reveal roles of water in aqueous phase reforming of glycerol.