Ab initiodetermination of structure-property relationships in alloy nanoparticles

Mueller, Tim

doi:10.1103/physrevb.86.144201

Cited by 37 publications

(39 citation statements)

References 30 publications

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“…are three coefficients that we fit to DFT calculations. We note that The ECI of the cluster expansions were fit using the Bayesian method (45), which is particularly effective for nanoparticles and surfaces (5,46). The determination of the regularization matrix, Λ , for the slab cluster expansion is complicated by the fact that the lattice-parameter-dependent slab cluster expansion contains three coefficients for each cluster orbit (Eq.…”

Section: Methodsmentioning

confidence: 99%

Computationally Generated Maps of Surface Structures and Catalytic Activities for Alloy Phase Diagrams

Cao¹,

Niu²,

Mueller³

2019

Preprint

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To facilitate the rational design of alloy catalysts, we introduce a method for rapidly calculating the structure and catalytic properties of a substitutional alloy surface that is in equilibrium with the underlying bulk phase. We implement our method by developing a way to generate surface cluster expansions that explicitly account for the lattice parameter of the bulk structure. This approach makes it possible to computationally map the structure and catalytic activity of an alloy surface at every point in the alloy phase diagram, enabling the identification of synthesis conditions likely to result in highly active catalysts. We demonstrate our approach by analyzing Pt-rich Pt–Ni catalysts for the oxygen reduction reaction, finding two regions in the phase diagram that are predicted to result in highly active catalysts. Our analysis indicates that the Pt3Ni(111) surface, which has the highest known specific activity for the oxygen reduction reaction, is likely able to achieve its high activity through the formation of an intermetallic phase with L12 order. We use the generated surface structure and catalytic activity maps to demonstrate how the intermetallic nature of this phase leads to high catalytic activity and discuss how the underlying principles can be used in catalysis design. We further discuss the importance of surface phases and demonstrate how they can dramatically affect catalytic activity.

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Section: Methodsmentioning

confidence: 99%

Computationally Generated Maps of Surface Structures and Catalytic Activities for Alloy Phase Diagrams

Cao¹,

Niu²,

Mueller³

2019

Preprint

Self Cite

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“…Meteorological and hydrological droughts are inter-related through interactions that happen within the water cycle (Van Loon and Laaha, 2015). Generally speaking, any higher than normal net evaporation rates over the oceans can change precipitation rates on land increasing continental water storage (Mueller et al, 2012). In contrast, a shortage in precipitation over land, along with a higher evaporation caused by a meteorological drought may lead to shortage in continental water storage and cause a hydrological drought (e.g., Wilhite, 2000;Tallaksen et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Since the launch of the Gravity Recovery And Climate Experiment (GRACE, Tapley et al, 2004) satellite gravity mission in 2002, drought monitoring studies have been using its estimates of Terrestrial Water Storage (TWS, a vertical integration of surface water, soil moisture, groundwater, and biomass water content) changes to understand global and regional hydrological processes (Chen et al, 2009;Rodell et al, 2009;Frappart et al, 2012;Houborg et al, 2012;Li et al, 2012;Mueller et al, 2012;Long et al, 2013;Thomas et al, 2014;Khaki et al, 2017). For example, Yirdaw et al (2008Yirdaw et al ( ) investigated 2002Yirdaw et al ( -2003 droughts in the Saskatchewan River basin.…”

Section: Introductionmentioning

confidence: 99%

Understanding the global hydrological droughts of 2003–2016 and their relationships with teleconnections

Forootan

Khaki

Schumacher

et al. 2019

Science of The Total Environment

144

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Droughts often evolve gradually and cover large areas, and therefore, affect many people and activities. This motivates developing techniques to integrate different satellite observations, to cover large areas, and understand spatial and temporal variability of droughts. In this study, we apply probabilistic techniques to generate satellite derived meteorological, hydrological, and hydro-meteorological drought indices for the world's 156 major river basins covering 2003-2016. The data includes Terrestrial Water Storage (TWS) estimates from the Gravity Recovery And Climate Experiment (GRACE) mission, along with soil moisture, precipitation, and evapotranspiration reanalysis. Different drought characteristics of trends, occurrences, areal-extent, and frequencies corresponding to 3-, 6-, 12-, and 24-month timescales are extracted from these indices. Drought evolution within selected basins of Africa, America, and Asia is interpreted. Canonical Correlation Analysis (CCA) is then applied to find the relationship between global hydrometeorological droughts and satellite derived Sea Surface Temperature (SST) changes. This relationship is then used to extract regions, where droughts and teleconnections are

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“…In this way, complex energy landscapes as a function of configurational degrees of freedom can be reproduced by fitting to a relatively small number of first-principles electronic structure calculations. The approach has enabled accurate first-principles predictions of temperature-composition phase diagrams, [23][24][25][26][27][28][29][30][31][32][33][34] order-disorder phenomena, [35][36][37][38][39][40][41][42][43][44][45] and composition-dependent diffusion coefficients in alloys and complex inorganic compounds. [46][47][48][49][50][51][52] A cluster expansion is formulated as a linear series of cluster basis functions multiplied by constant expansion coefficients that are determined by the underlying chemistry and crystal structure of the multicomponent solid.…”

Section: Introductionmentioning

confidence: 99%

Machine-learning the configurational energy of multicomponent crystalline solids

Natarajan

Ven

2018

npj Comput Mater

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Machine learning tools such as neural networks and Gaussian process regression are increasingly being implemented in the development of atomistic potentials. Here, we develop a formalism to leverage such non-linear interpolation tools in describing properties dependent on occupation degrees of freedom in multicomponent solids. Symmetry-adapted cluster functions are used to differentiate distinct local orderings. These local features are used as input to neural networks that reproduce local properties such as the site energy. We apply the technique to reproduce a synthetic cluster expansion Hamiltonian with multi-body interactions, as well as the formation energies calculated from first-principles for the intercalation of lithium into TiS 2. The formalism and results presented here show that complex multi-body interactions may be approximated by non-linear models involving smaller clusters.

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Ab initiodetermination of structure-property relationships in alloy nanoparticles

Cited by 37 publications

References 30 publications

Computationally Generated Maps of Surface Structures and Catalytic Activities for Alloy Phase Diagrams

Computationally Generated Maps of Surface Structures and Catalytic Activities for Alloy Phase Diagrams

Understanding the global hydrological droughts of 2003–2016 and their relationships with teleconnections

Machine-learning the configurational energy of multicomponent crystalline solids

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