Modified genetic algorithm to model crystal structures: III. Determination of crystal structures allowing simultaneous molecular geometry relaxation

2003 ◽  
Vol 96 (4) ◽  
pp. 312-320 ◽  
Author(s):  
Victor E. Bazterra ◽  
Marta B. Ferraro ◽  
Julio C. Facelli
Keyword(s):  
Genetic Algorithm ◽  
Crystal Structures ◽  
Molecular Geometry ◽  
Model Crystal ◽  
Modified Genetic Algorithm

Modified genetic algorithm to model crystal structures. I. Benzene, naphthalene and anthracene

2002 ◽  
Vol 116 (14) ◽  
pp. 5984-5991 ◽  
Author(s):  
Victor E. Bazterra ◽  
Marta B. Ferraro ◽  
Julio C. Facelli
Keyword(s):  
Genetic Algorithm ◽  
Crystal Structures ◽  
Model Crystal ◽  
Modified Genetic Algorithm

FIDDLE. Simultaneous Indexing and Structure Solution from Powder Diffraction Data using a Genetic Algorithm and Correlation Functions

2019 ◽  
Author(s):  
Carmen Guguta ◽  
Jan M.M. Smits ◽  
Rene de Gelder
Keyword(s):  
Genetic Algorithm ◽  
Powder Diffraction ◽  
Diffraction Data ◽  
Correlation Functions ◽  
Cross Correlation ◽  
Simultaneous Optimization ◽  
Powder Diffraction Data ◽  
Structure Solution ◽  
Matching Technique

A method for the determination of crystal structures from powder diffraction data is presented that circumvents the difficulties associated with separate indexing. For the simultaneous optimization of the parameters that describe a crystal structure a genetic algorithm is used together with a pattern matching technique based on auto and cross correlation functions.<br>

scholarly journals Full spectrum and genetic algorithm-selected spectrum-based chemometric methods for simultaneous determination of azilsartan medoxomil, chlorthalidone, and azilsartan: Development, validation, and application on commercial dosage form

2021 ◽  
Vol 19 (1) ◽  
pp. 205-213
Author(s):  
Hany W. Darwish ◽  
Abdulrahman A. Al Majed ◽  
Ibrahim A. Al-Suwaidan ◽  
Ibrahim A. Darwish ◽  
Ahmed H. Bakheit ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simultaneous Determination ◽  
Predictive Power ◽  
Principal Component Regression ◽  
Selection Procedure ◽  
Principal Component ◽  
Chemometric Methods ◽  
Full Spectrum ◽  
Azilsartan Medoxomil

Abstract Five various chemometric methods were established for the simultaneous determination of azilsartan medoxomil (AZM) and chlorthalidone in the presence of azilsartan which is the core impurity of AZM. The full spectrum-based chemometric techniques, namely partial least squares (PLS), principal component regression, and artificial neural networks (ANN), were among the applied methods. Besides, the ANN and PLS were the other two methods that were extended by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. The models were developed by applying a multilevel multifactor experimental design. The predictive power of the suggested models was evaluated through a validation set containing nine mixtures with different ratios of the three analytes. For the analysis of Edarbyclor® tablets, all the proposed procedures were applied and the best results were achieved in the case of ANN, GA-ANN, and GA-PLS methods. The findings of the three methods were revealed as the quantitative tool for the analysis of the three components without any intrusion from the co-formulated excipient and without prior separation procedures. Moreover, the GA impact on strengthening the predictive power of ANN- and PLS-based models was also highlighted.

Global geometry optimization of (Ar)n and B(Ar)n clusters using a modified genetic algorithm

1996 ◽  
Vol 104 (7) ◽  
pp. 2684-2691 ◽  
Author(s):  
Susan K. Gregurick ◽  
Millard H. Alexander ◽  
Bernd Hartke
Keyword(s):  
Genetic Algorithm ◽  
Geometry Optimization ◽  
Global Geometry ◽  
Modified Genetic Algorithm
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