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  • Author or Editor: D. Noid x
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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%).

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Thermal analysis by classical molecular dynamics simulations is discussed on hand of heat capacity of crystals of 9600 atoms. The differences between quantum mechanical and classical mechanical calculations are shown. Anharmonicity is proven to be an important factor. Finally, it is found that defects contribute to an increase in heat capacity before melting. The energy of conformational gauche defects within the crystal is only about 10% due to internal rotation. The other energy must be generated by cooperative strain. The conclusion is that the next generation of faster computers may permit wider use of molecular dynamics simulations in support of the interpretation of thermal analysis.

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