Effect of the surface composition of silica supported Pt−Ru Bimetallic catalysts on methane-deuterium exchange reaction

1986 ◽  
Vol 32 (2) ◽  
pp. 487-492 ◽  
Author(s):  
H. Miura ◽  
Y. Ushikubo ◽  
K. Sugiyama ◽  
T. Matsuda
Keyword(s):  
Exchange Reaction ◽  
Bimetallic Catalysts ◽  
Surface Composition ◽  
Deuterium Exchange

scholarly journals Machine Learning Prediction of Surface Segregation Energies on Low Index Bimetallic Surfaces

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2182
Author(s):  
Damilola Ologunagba ◽  
Shyam Kattel
Keyword(s):  
Machine Learning ◽  
Dft Calculations ◽  
Bimetallic Catalysts ◽  
Density Functional ◽  
Surface Segregation ◽  
Surface Composition ◽  
Bulk Composition ◽  
Statistical Machine Learning ◽  
Bimetallic Surfaces ◽  
Computing Platforms

Surface chemical composition of bimetallic catalysts can differ from the bulk composition because of the segregation of the alloy components. Thus, it is very useful to know how the different components are arranged on the surface of catalysts to gain a fundamental understanding of the catalysis occurring on bimetallic surfaces. First-principles density functional theory (DFT) calculations can provide deeper insight into the surface segregation behavior and help understand the surface composition on bimetallic surfaces. However, the DFT calculations are computationally demanding and require large computing platforms. In this regard, statistical/machine learning methods provide a quick and alternative approach to study materials properties. Here, we trained previously reported surface segregation energies on low index surfaces of bimetallic catalysts using various linear and non-linear statistical methods to find a correlation between surface segregation energies and elemental properties. The results revealed that the surface segregation energies on low index bimetallic surfaces can be predicted using fundamental elemental properties.

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