A Hybrid Flower Pollination and Genetic Algorithm for Minimizing the Non-Convex Potential Energy of Molecular Structure

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Tawhid Mohamed A ◽  
Ali Ahmed F
Keyword(s):  
Molecular Structure ◽  
Genetic Algorithm ◽  
Potential Energy ◽  
Flower Pollination

scholarly journals Exploring the potential energy surface of small lead clusters using the gradient embedded genetic algorithm and an adequate treatment of relativistic effects

RSC Advances ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 145-152 ◽  
Author(s):  
Walter A. Rabanal-León ◽  
William Tiznado ◽  
Edison Osorio ◽  
Franklin Ferraro
Keyword(s):  
Genetic Algorithm ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Crucial Role ◽  
Energy Surface ◽  
Relativistic Effects ◽  
Spin Orbit ◽  
Adequate Treatment ◽  
Structural Assignment

Theoretical inclusion of relativistic effects (scalar and spin–orbit) play a crucial role to assure an adequate structural assignment on lead clusters.

scholarly journals First-Principles Molecular Structure Search with a Genetic Algorithm

2015 ◽  
Vol 55 (11) ◽  
pp. 2338-2348 ◽  
Author(s):  
Adriana Supady ◽  
Volker Blum ◽  
Carsten Baldauf
Keyword(s):  
Molecular Structure ◽  
Genetic Algorithm ◽  
First Principles ◽  
Structure Search

The molecular structure and the analytical potential energy function of S 2 − and S 3 −

2007 ◽  
Vol 16 (8) ◽  
pp. 2356-2360 ◽  
Author(s):  
Liu Yu-Fang ◽  
Li Jun-Yu ◽  
Han Xiao-Qin ◽  
Sun Jin-Feng
Keyword(s):  
Molecular Structure ◽  
Potential Energy ◽  
Energy Function ◽  
Potential Energy Function ◽  
Analytical Potential

Conformational space analysis of neutral and protonated glycine using a genetic algorithm for multi-modal search

2014 ◽  
Vol 13 (08) ◽  
pp. 1450067 ◽  
Author(s):  
B. El Merbouh ◽  
M. Bourjila ◽  
R. Tijar ◽  
R. Drissi El Bouzaidi ◽  
A. El Gridani ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Protonated Form ◽  
Conformational Space ◽  
Empirical Methods ◽  
Space Analysis ◽  
Semi Empirical

The genetic algorithm based on the Multi-Niche Crowding (MNC) method is used with the semi-empirical methods AM1 and PM3 in order to scan the potential energy surface (PES) of neutral and protonated glycine. The algorithm is implemented as a package of programs interfaced with MOPAC and piloted by scripts. Both methods AM1 and PM3 located six minima on the PES of neutral glycine and seven on the protonated glycine one, of which three are those of the N -protonated form and four of the O -protonated one.

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