atomic distribution
Recently Published Documents


TOTAL DOCUMENTS

155
(FIVE YEARS 7)

H-INDEX

32
(FIVE YEARS 1)

2021 ◽  
Author(s):  
Lorenz Cederbaum ◽  
Alexander Kuleff

Abstract The impact of quantum light on interatomic Coulombic decay (ICD) is investigated. In ICD the excess energy of an excited atom A is efficiently utilized to ionize a neighboring atom B. In quantum light an ensemble of atoms A form polaritonic states which can undergo ICD with B. It is shown that this process is dramatically altered compared to classical ICD. The ICD rate depends sensitively on the atomic distribution and orientation of the ensemble. General consequences are discussed.


2020 ◽  
Vol 26 (S2) ◽  
pp. 1228-1230
Author(s):  
Hsien-Lien Huang ◽  
Jared Johnson ◽  
Jinwoo Hwang

2020 ◽  
Vol 8 (47) ◽  
pp. 25131-25141
Author(s):  
In Hye Kwak ◽  
Tekalign Terfa Debela ◽  
Ik Seon Kwon ◽  
Jaemin Seo ◽  
Seung Jo Yoo ◽  
...  

Anisotropic atomic distribution of Re1−xMoxS2 alloy nanosheets enhanced their electrocatalytic performance toward the hydrogen evolution reaction.


2019 ◽  
Vol 515 ◽  
pp. 135-139
Author(s):  
Boran Tao ◽  
Baifeng Luan ◽  
Risheng Qiu ◽  
Qiang Fang ◽  
Lingfei Cao ◽  
...  

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.


2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.


2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.


Sign in / Sign up

Export Citation Format

Share Document