scholarly journals Performance of a Non-Local van der Waals Density Functional on the Dissociation of H2 on Metal Surfaces

2015 ◽  
Vol 119 (50) ◽  
pp. 12146-12158 ◽  
Author(s):  
Mark Wijzenbroek ◽  
David M. Klein ◽  
Bauke Smits ◽  
Mark F. Somers ◽  
Geert-Jan Kroes
Keyword(s):  
Metal Surfaces ◽  
Density Functional ◽  
Van Der Waals ◽  
Non Local

scholarly journals Vacancy patterning and patterning vacancies: controlled self-assembly of fullerenes on metal surfaces

Nanoscale ◽  
2014 ◽  
Vol 6 (18) ◽  
pp. 10850-10858 ◽  
Author(s):  
Alexander Kaiser ◽  
Francesc Viñes ◽  
Francesc Illas ◽  
Marcel Ritter ◽  
Frank Hagelberg ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Metal Surface ◽  
Self Assembly ◽  
Metal Surfaces ◽  
Density Functional ◽  
Van Der Waals ◽  
Van Der Waals Interactions ◽  
Density Functional Theory Study ◽  
Functional Theory ◽  
Anchor Points

A density functional theory study accounting for van der Waals interactions reveals the potential of either using metal surface vacancies as anchor points for the design of 2D patterns of adsorbate molecules or vice versa using adsorbate monolayers to design vacancy patterns.

NON-LOCAL ELECTROMAGNETIC EFFECTS AT METAL SURFACES

1983 ◽  
Vol 44 (C10) ◽  
pp. C10-305-C10-314
Author(s):  
S. Lundqvist ◽  
P. Apell
Keyword(s):  
Metal Surfaces ◽  
Electromagnetic Effects ◽  
Non Local

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

2018 ◽  
Author(s):  
Sherif Tawfik ◽  
Olexandr Isayev ◽  
Catherine Stampfl ◽  
Joseph Shapter ◽  
David Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Band Gap ◽  
Density Functional ◽  
2D Materials ◽  
Van Der Waals ◽  
Building Blocks ◽  
Machine Learning Techniques ◽  
Interlayer Distance ◽  
Computational Screening ◽  
Wide Range

Materials constructed from different van der Waals two-dimensional (2D) heterostructures offer a wide range of benefits, but these systems have been little studied because of their experimental and computational complextiy, and because of the very large number of possible combinations of 2D building blocks. The simulation of the interface between two different 2D materials is computationally challenging due to the lattice mismatch problem, which sometimes necessitates the creation of very large simulation cells for performing density-functional theory (DFT) calculations. Here we use a combination of DFT, linear regression and machine learning techniques in order to rapidly determine the interlayer distance between two different 2D heterostructures that are stacked in a bilayer heterostructure, as well as the band gap of the bilayer. Our work provides an excellent proof of concept by quickly and accurately predicting a structural property (the interlayer distance) and an electronic property (the band gap) for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.

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