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CuZrO 3 has been hypothesized to be a catalytic material with potential applications for CO 2 reduction. Unfortunately, this material has received limited attention in the literature, and to the best of our knowledge the exact crystal structure is still unknown. To address this challenge, we utilize several different structural prediction techniques in concert, including the Universal Structure Predictor: Evolutionary Xtallography (USPEX), the Materials Project Structure Predictor, and the Open Quantum Materials Database (OQMD). Leveraging these structural prediction techniques in conjunction with Density-Functional Theory (DFT) calculations, we determine a possible structure for CuZrO 3 , which resembles a “sandwich” morphology. Our calculations reveal that this new structure is significantly lower in energy than a previously hypothesized perovskite structure, albeit it still has a thermodynamic preference to decompose into CuO and ZrO 2 . In addition, we experimentally tried to synthesize CuZrO 3 based on literature reports and compared computational to experimental X-ray Diffraction (XRD) patterns confirming that the final product is a mixture of CuO and ZrO 2 . Finally, we conducted a computational surface energetics and CO 2 adsorption study on our discovered sandwich morphology, demonstrating that CO 2 can adsorb and activate on the material. However, these CO 2 adsorption results deviate from previously reported results further confirming that the CuZrO 3 is a metastable form and may not be experimentally accessible as a well-mixed oxide, since phase segregation to CuO and ZrO 2 is preferred. Taken together, our combined computational and experimental study provides evidence that the synthesis of CuZrO 3 is extremely difficult and if this oxide exists, it should have a sandwich-like morphology. 

Determining the energetically most favorable structure of nanoparticles is a fundamentally important task towards understanding their stability. In the case of bimetallic nanoclusters, their vast configurational space makes it especially challenging to find the global energy optimum via experimental or computational screening. To that end, this work proposes a two-step optimization-based design framework to address this hard combinatorial problem. Given a nanocluster of fixed shape, a rigorous mixed-integer linear programming model is formulated based on a bond-centric cohesive energy function to identify the most cohesive bimetallic configuration for a given composition. This capability is coupled with a metaheuristic strategy that searches over the space of nanocluster shapes to obtain optimal structures. We apply our proposed methodology on AgCu, AuAg and CuAu systems, quantifying how the size and composition of a nanocluster influences its overall cohesion. Furthermore, we observe various synergistic effects between Cu and Au in promoting cohesive energy, while multiple segregation patterns are identified in all three studied binary systems. Our methodology serves as an efficient computational tool for investigating bimetallic nanoclusters stability properties as well as provides model nanoclusters for further investigations. 

Small nanoparticles, a.k.a. nanoclusters, of transition metals have been studied extensively for a wide range of applications due to their highly tunable properties dependent on size, structure, and composition. For these small particles, there has been considerable effort towards theoretically predicting what is the most energetically favorable arrangement of atoms when forming a nanocluster. In this work, we develop a computational framework that couples density-functional theory calculations with mathematical optimization modeling to identify highly stable, mono-metallic transition metal nanoclusters of various sizes. This is accomplished by devising and solving a rigorous mathematical optimization model that maximizes a general cohesive energy function to obtain nanocluster structures of provably maximal cohesiveness. We then utilize density-functional theory calculations and error term regression to identify model corrections that are necessary to account with better accuracy for different transition metals. This allows us to encode metal-specific, analytical functions for cohesive energy into a mathematical optimization-based framework that can accurately predict which nanocluster geometries will be most cohesive according to density-functional theory calculations. We employ our framework in the context of Ag, Au, Cu, Pd and Pt, and we present sequences of highly cohesive nanoclusters for sizes up to 100 atoms, yielding insights into structures that might be experimentally accessible and/or structures that could be used as model nanoclusters for further study. 

Metal nanoparticles have received substantial attention in the past decades for their applications in numerous areas, including medicine, catalysis, energy, and the environment. Despite these applications, the fundamentals of adsorption on nanoparticle surfaces as a function of nanoparticle size, shape, metal composition, and type of adsorbate are yet to be found. Herein, we introduce the first universal adsorption model that accounts for detailed nanoparticle structural characteristics, metal composition, and different adsorbates by combining first principles calculations with machine learning. Our model fits a large number of data and accurately predicts adsorption trends on nanoparticles (both monometallic and alloy) that have not been previously seen. In addition to its application power, the model is simple and uses tabulated and rapidly calculated data for metals and adsorbates. We connect adsorption with stability behavior of nanoparticles, thus advancing the design of optimal nanoparticles for applications of interest, such as catalysis.