A systematic modeling methodology of deep neural network‐based structure‐property relationship for rapid and reliable prediction on flashpoints

AIChE Journal ◽  
2021 ◽  
Author(s):  
Huaqiang Wen ◽  
Yang Su ◽  
Zihao Wang ◽  
Saimeng Jin ◽  
Jingzheng Ren ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Structure Property ◽  
Reliable Prediction ◽  
Property Relationship ◽  
Modeling Methodology ◽  
Structure Property Relationship ◽  
Systematic Modeling

scholarly journals A New Accurate Neural Network Quantitative Structure- Property Relationship for Prediction of θ (Lower Critical Solution Temperature) of Polymer Solutions

e-Polymers ◽  
2007 ◽  
Vol 7 (1) ◽  
Author(s):  
Farhad Gharagheizi
Keyword(s):  
Neural Network ◽  
Molecular Descriptors ◽  
Polymer Solutions ◽  
Solution Temperature ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Relationship Model

Abstract In this study, a new neural network quantitative structure-property relationship model for prediction of θ (LCST ) of polymer solutions is presented. The parameters of this model are eight molecular descriptors which are calculated only from the chemical structure of polymer and solvent. These eight molecular descriptors were selected from 3328 molecular descriptors of polymer and solvent available in polymer solution by genetic algorithm-based multivariate linear regression (GA-MLR) technique. The obtained neural network model can predict the θ (LCST ) of 169 polymer solutions with mean relative error of 1.67% and squared correlation coefficient of 0.9736.

General QSPR Protocol for Atomic/Inter-atomic Properties Predictions: Fragments based Graph Convolutional Neural Network (F-GCN)

2021 ◽  
Author(s):  
Peng Gao ◽  
Jie Zhang ◽  
Hongbo Qiu ◽  
Shuaifei Zhao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Atomic Properties ◽  
Property Relationship ◽  
Structure Property Relationship

In this study, a general quantitative structure-property relationship (QSPR) protocol, fragments based graph convolutional neural network (F-GCN), was developed for atomic and inter-atomic properties predictions. We applied this novel artificial...

Exploration of Bis(Arylimidazole) Iridium Picolinate Complexes

2017 ◽  
Author(s):  
Aron Huckaba ◽  
sadig aghazada ◽  
iwan zimmermann ◽  
giulia grancini ◽  
natalia gasilova ◽  
...  
Keyword(s):  
Cyclic Voltammetry ◽  
Photophysical Properties ◽  
Structure Property ◽  
Aryl Group ◽  
Ligand Modification ◽  
Property Relationship ◽  
Structure Property Relationship

The straightforward synthesis and photophysical properties of a new series of heteroleptic Iridium (III) bis(2-arylimidazole) picolinate complexes is reported. Each complex has been characterized by NMR, UV-Vis, cyclic voltammetry, and the emissive properties of each is described. By systematically modifying first the cyclometallating aryl group on the arylimidazole ligand and then the picolinate ligand, the ramifications of ligand modification in these complexes was better understood through the construction of a structure-property relationship.

Exploration of Bis(Arylimidazole) Iridium Picolinate Complexes

2017 ◽  
Author(s):  
Aron Huckaba ◽  
sadig aghazada ◽  
iwan zimmermann ◽  
giulia grancini ◽  
natalia gasilova ◽  
...  
Keyword(s):  
Cyclic Voltammetry ◽  
Photophysical Properties ◽  
Structure Property ◽  
Aryl Group ◽  
Ligand Modification ◽  
Property Relationship ◽  
Structure Property Relationship

The straightforward synthesis and photophysical properties of a new series of heteroleptic Iridium (III) bis(2-arylimidazole) picolinate complexes is reported. Each complex has been characterized by NMR, UV-Vis, cyclic voltammetry, and the emissive properties of each is described. By systematically modifying first the cyclometallating aryl group on the arylimidazole ligand and then the picolinate ligand, the ramifications of ligand modification in these complexes was better understood through the construction of a structure-property relationship.

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.

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