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Perovskite oxides (ternary chemical formula ABO₃) are a diverse class of materials with applications including heterogeneous catalysis, solid-oxide fuel cells, thermochemical conversion, and oxygen transport membranes. However, their multicomponent (chemical formula$${A}_{x}{A}_{1-x}^{\text{'}}{B}_{y}{B}_{1-y}^{\text{'}}{O}_{3}$$$A_x{A}_{1-x}^{\text{'}}{B}_y{B}_{1-y}^{\text{'}}{O}_3$) chemical space is underexplored due to the immense number of possible compositions. To expand the number of computed$${A}_{x}{A}_{1-x}^{{\prime} }{B}_{y}{B}_{1-y}^{{\prime} }{O}_{3}$$$A_x{A}_{1-x}^{\prime }{B}_y{B}_{1-y}^{\prime }{O}_3$compounds we report a dataset of 66,516 theoretical multinary oxides, 59,708 of which are perovskites. First, 69,407$${A}_{0.5}{A}_{0.5}^{{\prime} }{B}_{0.5}{B}_{0.5}^{{\prime} }{O}_{3}$$$A_{0.5}{A}_{0.5}^{\prime }{B}_{0.5}{B}_{0.5}^{\prime }{O}_3$compositions were generated in thea⁻b⁺a⁻Glazer tilting mode using the computationally-inexpensive Structure Prediction and Diagnostic Software (SPuDS) program. Next, we optimized these structures with density functional theory (DFT) using parameters compatible with the Materials Project (MP) database. Our dataset contains these optimized structures and their formation (ΔH_f) and decomposition enthalpies (ΔH_d) computed relative to MP tabulated elemental references and competing phases, respectively. This dataset can be mined, used to train machine learning models, and rapidly and systematically expanded by optimizing more SPuDS-generated$${A}_{0.5}{A}_{0.5}^{{\prime} }{B}_{0.5}{B}_{0.5}^{{\prime} }{O}_{3}$$$A_{0.5}{A}_{0.5}^{\prime }{B}_{0.5}{B}_{0.5}^{\prime }{O}_3$perovskite structures using MP-compatible DFT calculations.

 

The electrochemical nitrogen reduction reaction (NRR) is a promising route to enable carbon-free ammonia production. However, this reaction is limited by the poor activity and selectivity of current catalysts. The rational design of superior NRR electrocatalysts requires a detailed mechanistic understanding of current material limitations to inform how these might be overcome. The current understanding of how scaling limits NRR on metal catalysts is predicated on a simplified reaction pathway that considers only proton-coupled electron transfer (PCET) steps. Here, we apply grand-canonical density functional theory to investigate a more comprehensive NRR mechanism that includes both electrochemical and chemical steps on 30 metal surfaces in solvent under an applied potential. We applied Φmax, a grandcanonical adaptation of the Gmax thermodynamic descriptor, to evaluate trends in catalyst activity. This approach produces a Φmax “volcano” diagram for NRR activity scaling on metals that qualitatively differs from the scaling relations identified when only PCET steps are considered. NH3* desorption was found to limit the NRR activity for materials at the top of the volcano and truncate the volcano’s peak at increasingly reducing potentials. These revised scaling relations may inform the rational design of superior NRR electrocatalysts. This approach is transferable to study other materials and reaction chemistries where both electrochemical and chemical steps are modeled under an applied potential. 

A high‐throughput computational framework to identify novel multinary perovskite redox mediators is presented, and this framework is applied to discover the Gd‐containing perovskite oxide compositions Gd₂BB′O_6,GdA′B₂O₆, and GdA′BB′O₆that split water. The computational scheme uses a sequence of empirical approaches to evaluate the stabilities, electronic properties, and oxygen vacancy thermodynamics of these materials, including contributions to the enthalpies and entropies of reduction, ΔH_TRand ΔS_TR. This scheme uses the machine‐learned descriptor τ to identify compositions that are likely stable as perovskites, the bond valence method to estimate the magnitude and phase of BO₆octahedral tilting and provide accurate initial estimates of perovskite geometries, and density functional theory including magnetic‐ and defect‐sampling to predict STCH‐relevant properties. Eighty‐three promising STCH candidate perovskite oxides down‐selected from 4392 Gd‐containing compositions are reported, three of which are referred to experimental collaborators for characterization and exhibit STCH activity. The results demonstrate that the high‐throughput computational scheme described herein—which is used to evaluate Gd‐containing compositions but can be applied to any multinary perovskite oxide compositional space(s) of interest—accelerates the discovery of novel STCH active redox mediators with reasonable computational expense.

 

A recent advance in the synthesis of alkenylated arenes was the demonstration that the Pd(OAc)2 catalyst precursor gives >95% selectivity toward styrene from ethylene and benzene under optimized conditions using excess Cu(II) carboxylate as the in situ oxidant [ Organometallics 2019, 38(19), 3532−3541]. To understand the mechanism underlying this catalysis, we applied density functional theory (DFT) calculations in combination with experimental studies. From DFT calculations, we determined the lowest-energy multimetallic Pd and Pd–Cu mixed metal species as possible catalyst precursors. From the various structures, we determined the cyclic heterotrinuclear complex PdCu2(μ-OAc)6 to be the global minimum in Gibbs free energy under conditions of excess Cu(II). For cyclic PdCu2(μ-OAc)6 and the parent [Pd(μ-OAc)2]3, we evaluated the barriers for benzene C–H activation through concerted metalation deprotonation (CMD). The PdCu2(μ-OAc)6 cyclic trimer leads to a CMD barrier of 33.5 kcal/mol, while the [Pd(μ-OAc)2]3 species leads to a larger CMD barrier at >35 kcal/mol. This decrease in the CMD barrier arises from the insertion of Cu(II) into the trimetallic species. Because cyclic PdCu2(μ-OAc)6 is likely the predominant species under experimental conditions (the Cu to Pd ratio is 480:1 at the start of catalysis) with a predicted CMD barrier within the range of the experimentally determined activation barrier, we propose that cyclic PdCu2(μ-OAc)6 is the Pd species responsible for catalysis and report a full reaction mechanism based on DFT calculations. For catalytic conversion of benzene and ethylene to styrene at 120 °C using Pd(OAc)2 as the catalyst precursor and Cu(OPiv)2 (OPiv = pivalate) as the oxidant, an induction period of ∼1 h was observed, followed by catalysis with a turnover frequency of ∼2.3 × 10–3 s–1. In situ1H NMR spectroscopy experiments indicate that during the induction period, Pd(OAc)2 is likely converted to cyclic PdCu2(η2-C2H4)3(μ-OPiv)6, which is consistent with the calculations and consistent with the proposal that the active catalyst is the ethylene-coordinated heterotrinuclear complex cyclic PdCu2(η2-C2H4)3(μ-OPiv)6.