Abstract
The glass transition temperature (Tg) is an important decision parameter when synthesizing polymeric compounds or when selecting their applicability domain. In this work, the glass transition temperature of more than 100 homopolymers with saturated backbones was predicted using a neuro-evolutive technique combining Artificial Neural Networks with a modified Bacterial Foraging Optimization Algorithm. In most cases, the selected polymers have a vinyl-type backbone substituted with various groups. A few samples with an oxygen atom in a linear non-vinyl hydrocarbon main chain were also considered. Eight structural, thermophysical, and entanglement properties estimated by the quantitative structure–property relationship (QSPR) method, along with other molecular descriptors reflecting polymer composition, were considered as input data for Artificial Neural Networks. The Tg’s neural model has a 7.30% average absolute error for the training data and 12.89% for the testing one. From the sensitivity analysis, it was found that cohesive energy, from all independent parameters, has the highest influence on the modeled output.
Highlights
Due to their high specific strength, lightweight, high performance, or easy processability, polymers are often chosen as the best option in different applications
Six compounds that cannot be included in the previpolyolefins, 7 halogenated polyolefins, 13 polystyrene-type compounds, 19 polyacryously mentioned classes, the majority of them being polyacrylamides with vinyl backbone lates, 29 polymethacrylates, polycyanoacrylates, and 12 homopolymers with hydroxyl are included in the subclass 4“others”
The AAEfor obtained for this validation set was and the correlation was. These results indicate that the additional validation set was 15.096% and the correlation was 0.901
Summary
Due to their high specific strength, lightweight, high performance, or easy processability, polymers are often chosen as the best option in different applications. The determination of the relationship between structure and properties can lead to advances in the field and can help in the endeavor to find materials with specific properties. A key parameter of polymers, the glass transition temperature (Tg), could be perceived either as an indicator of amorphous content, different degrees of polymerization, the presence of moisture or as additives in these materials. The Tg is not that easy to be determined neither in an experimental or a theoretical manner, because of the multiple factors that control this temperature. In view of the fact that the transition in a rubbery state occurs alongside a temperature range, assigning a unique value is problematic, especially for the wider domains.
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