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Abstract Surface segregation, whereby the surface composition of an alloy differs systematically from the bulk, has historically been hard to study, because it requires experimental and modeling methods that span alloy composition space. In this work, we study surface segregation in catalytically relevant noble and platinum‐group metal alloys with a focus on three ternary systems: AgAuCu, AuCuPd, and CuPdPt. We develop a data set of 2478 fcc slabs with those compositions including all three low‐index crystallographic orientations relaxed with Density Functional Theory using the PBEsol functional with D3 dispersion corrections. We fine‐tune a machine learning model on this data and use the model in a series of 1800 Monte Carlo simulations spanning ternary composition space for each surface orientation and ternary chemical system. The results of these simulations are validated against prior experimental surface segregation data collected using composition spread alloy films for AgAuCu and AuCuPd. Our findings reveal that simulations conducted using the (110) orientation most closely match experimentally observed surface segregation trends, and while predicted trends qualitatively match observation, biases in the PBEsol functional limit numeric accuracy. This study advances understanding of surface segregation and the utility of computational studies and highlights the need for further improvements in simulation accuracy. 

Abstract The degree of rate control (DRC) quantitatively identifies the kinetically relevant (sometimes known as rate‐limiting) steps of a complex reaction network. This concept relies on derivatives which are commonly implemented numerically, for example, with finite differences (FDs). Numerical derivatives are tedious to implement, and can be problematic, and unstable or unreliable. In this study, we demonstrate the use of automatic differentiation (AD) in the evaluation of the DRC. AD libraries are increasingly available through modern machine learning frameworks. Compared with the FDs, AD provides solutions with higher accuracy with lower computational cost. We demonstrate applications in steady‐state and transient kinetics. Furthermore, we illustrate a hybrid local‐global sensitivity analysis method, the distributed evaluation of local sensitivity analysis, to assess the importance of kinetic parameters over an uncertain space. This method also benefits from AD to obtain high‐quality results efficiently. 

With the popularity of machine learning growing in the field of catalysis there are increasing numbers of catalyst databases becoming available. These databases provide us with the opportunity to search for catalysts with desired properties, which could lead to the discovery of new catalysts. However, while there are search methods for molecules based on similarity metrics, for solid-state catalyst systems there is not yet a straightforward search method. In this work, we propose a neural network embeddings based similarity search method that is applicable for both molecules and solid-state catalyst systems. We illustrate how the search method works and show search examples for the QM9, Materials Project (MP) and Open Catalyst 2020 (OC20) databases. We show that the configurations found present similarity in terms of geometry, composition, energy and in the electronic density of states. These results imply the neural network embeddings have encoded effective information that could be used to retrieve molecules and materials with similar properties. 

Surface segregation is a phenomenon common to all multicomponent materials and one that plays a critical role in determining their surface properties. Comprehensive studies of surface segregation versus bulk composition in ternary alloys have been prohibitive because of the need to study many different compositions. In this work, high-throughput low-energy He+ ionscattering spectra and energy-dispersive X-ray spectra were collected from a CuxAuyPd1−x−y composition spread alloy film under ultrahigh vacuum conditions. These have been used to quantify surface segregation across the entire CuxAuyPd1−x−y composition space (x = 0 → 1 and y = 0 → 1 − x). Surface compositions at 164 different bulk compositions were measured at 500 and 600 K. At both temperatures, Au shows the greatest tendency for segregation to the top-most surface while Pd is always depleted from the surface. Higher temperatures enhance the Au segregation. Segregation at most of the binary alloy bulk compositions matches with observations previously reported in the literature. However, surface compositions in the CuPd B2 composition region reveal segregation profiles that are nonmonotonic in bulk alloy composition. These were not observable in prior studies because of their limited resolution of composition space. An extended Langmuir−MacLean model, which describes ternary alloy segregation, has been used to analyze experimental data from the ternary alloys and to estimate pair-wise segregation free energies and segregation equilibrium constants. The ability to study surface segregation across the ternary alloy composition space with high-throughput methods has been validated, and the impact of bulk alloy phase on surface segregation is demonstrated and discussed.