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| - Prediction of Glass Transition Temperatures from Monomer and Repeat Unit StructureUsing Computational Neural Networks
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| - Quantitative structure−property relationships (QSPR) are developed to correlate glass transition temperaturesand chemical structure. Both monomer and repeat unit structures are used to build several QSPR models forParts 1 and 2 of this study, respectively. Models are developed using numerical descriptors, which encodeimportant information about chemical structure (topological, electronic, and geometric). Multiple linearregression analysis (MLRA) and computational neural networks (CNNs) are used to generate the modelsafter descriptor generation. Optimization routines (simulated annealing and genetic algorithm) are utilizedto find information-rich subsets of descriptors for prediction. A 10-descriptor CNN model was found to beoptimal in predicting Tg values using the monomer structure (Part 1) for 165 polymers. A committee of 10CNNs produced a training set rms error of 10.1K (r2 = 0.98) and a prediction set rms error of 21.7K (r2 =0.92). An 11-descriptor CNN model was developed for 251 polymers using the repeat unit structure (Part2). A committee of CNNs produced a training set rms error of 21.1K (r2 = 0.96) and a prediction set rmserror of 21.9K (r2 = 0.96).
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