Quantitative structure-property relationship modeling of small organic molecules for solar cells applications

2017 ◽  
Vol 32 (2) ◽  
pp. e2957 ◽  
Author(s):  
Sara Tortorella ◽  
Filippo De Angelis ◽  
Gabriele Cruciani
Keyword(s):  
Solar Cells ◽  
Organic Molecules ◽  
Quantitative Structure ◽  
Structure Property ◽  
Small Organic Molecules ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Structure Property Relationship

Quantitative structure–property relationship modeling of ruthenium sensitizers for solar cells applications: novel tools for designing promising candidates

RSC Advances ◽  
2015 ◽  
Vol 5 (30) ◽  
pp. 23865-23873 ◽  
Author(s):  
Sara Tortorella ◽  
Gabriele Marotta ◽  
Gabriele Cruciani ◽  
Filippo De Angelis
Keyword(s):  
Solar Cells ◽  
Dye Sensitized Solar Cells ◽  
Quantitative Structure ◽  
Trial And Error ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Dye Sensitized ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Sensitized Solar Cells

To date, the most common way of screening new potential sensitizers for dye sensitized solar cells is via the traditional time and money consuming trial and error approach.

Quantitative Structure- Property Relationship (QSPR) Investigation of Camptothecin Drugs Derivatives

2018 ◽  
Vol 21 (7) ◽  
pp. 533-542 ◽  
Author(s):  
Neda Ahmadinejad ◽  
Fatemeh Shafiei ◽  
Tahereh Momeni Isfahani
Keyword(s):  
Thermodynamic Properties ◽  
Predictive Ability ◽  
Total Sample ◽  
Basis Sets ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Chemical Structures ◽  
Property Relationship ◽  
Structure Property Relationship

Aim and Objective: Quantitative Structure- Property Relationship (QSPR) has been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been developed for modeling and predicting thermodynamic properties of 76 camptothecin derivatives using molecular descriptors. Materials and Methods: Thermodynamic properties of camptothecin such as the thermal energy, entropy and heat capacity were calculated at Hartree–Fock level of theory and 3-21G basis sets by Gaussian 09. Results: The appropriate descriptors for the studied properties are computed and optimized by the genetic algorithms (GA) and multiple linear regressions (MLR) method among the descriptors derived from the Dragon software. Leave-One-Out Cross-Validation (LOOCV) is used to evaluate predictive models by partitioning the total sample into training and test sets. Conclusion: The predictive ability of the models was found to be satisfactory and could be used for predicting thermodynamic properties of camptothecin derivatives.

Sign in / Sign up

Export Citation Format

Share Document