An Improved Structure−Property Model for Predicting Melting-Point Temperatures
| Autor: | Srinivasa S. Godavarthy, Khaled A.M. Gasem, and Robert L. Robinson |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Nonlinear system
Quantitative structure–activity relationship Work (thermodynamics) Materials science Artificial neural network General Chemical Engineering Molecular descriptor Melting point Structure property Thermodynamics General Chemistry Sensitivity (control systems) Industrial and Manufacturing Engineering |
| Zdroj: | Industrial & Engineering Chemistry Research. 45:5117-5126 |
| ISSN: | 1520-5045 0888-5885 |
| DOI: | 10.1021/ie051130p |
| Popis: | Physical properties and thermodynamic data are essential inputs to all computer-aided molecular design applications. Basic properties, such as the melting-point temperature, are essential for developing custom chemicals with desired thermophysical behavior. Currently, accurate correlations for the melting-point temperature are limited, including recent attempts to use quantitative structure−property relationships (QSPR). The lack of a comprehensive melting-point model can be attributed to (a) the sensitivity of the melting point to subtle variations in molecular structure and (b) the inability of existing molecular descriptors to account satisfactorily for all factors that influence the melting-point behavior. In this work, we present a new QSPR model for predicting the melting-point temperature of a diverse organic dataset. The model benefits from the inclusion of novel nonlinear descriptors developed through the use of robust genetic algorithms (GAs) and neural networks. Three new descriptors were devel... |
| Databáze: | OpenAIRE |
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