Abstract
A general purpose computational paradigm using neural networks is shown to be capable of efficiently predicting properties of polymeric compounds based on the structure and composition of the monomeric repeat unit. Results are discussed for the prediction of the heat capacity, glass transition temperature, melting temperature, change in the heat capacity at the glass transition temperature, degradation temperature, tensile strength and modulus, ultimate elongation, and compressive strength for 11 different families of polymers. The accuracies of the predictions range from 1-13% average absolute error. The worst results were obtained for the mechanical properties (tensile strength and modulus: 13%, 7% elongation: 12%, and compressive strength: 8%) and the best results for the thermal properties (heat capacity, glass transition temperature, and melting point: <4%). A simple modification to the overall method is devised to better take into account the fact that the mechanical properties are experimentally determined with a fairly large range (due to variability in measurement procedures and especially the sample). This modification treats the bounds on the range for the mechanical properties as complex numbers (complex, modular neural networks) and leads to more rapid optimization with a smaller average error (reduced by 3%).
Original language | English |
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Pages (from-to) | 833-851 |
Number of pages | 19 |
Journal | Journal of Thermal Analysis |
Volume | 46 |
Issue number | 3-4 |
DOIs | |
State | Published - 1996 |
Keywords
- Complex backpropagation
- Computational neural networks
- Molecular structure
- Physical and mechanical properties
- Polymeric materials
- Quantitative structure-property relationships
- Statistical regression
- Unsupervised learning