Prediction of polyamide properties using quantum-chemical methods and BP artificial neural networks (original) (raw)

2006

Abstract

Quantitative structure -property relationships (QSPR) for glass translation temperatures (T g), density (ρ) and indices of refraction (n) of the polyamides have been determined. All descriptors are calculated from molecular structures at the B3LYP/6-31G(d) level. These QSPR models are generated by two methods: multiple linear regression (MLR) and error back-propagation artificial neural networks (BPANN). The model obtained by MLR is used for the calculations of T g (R training=0.9074, SDtraining=22.4687, R test=0.8898, SDtest=23.2417), ρ (R training=0.9474, SDtraining=0.0422, R test=0.8928, SD test=0.0422), n (R training=0.9298, SDtraining=0.0204, R test=0.9095, SDtest=0.0274). The model obtained by BPANN is used for the calculations of T g (R training=0.9273, SDtraining=14.8988, R test=0.8989, SDtest=16.4396), ρ (R training=0.9523, SDtraining=0.0466, R test=0.9014, SDtest=0.0512), n (R training=0.9401, SDtraining=0.0131, R test=0.9445, SDtest=0.0179). These results demonstrate that the MLR and BPANN methods can be used to predict T g, ρ and n. The more accurate predicted results are obtained from BPANN. Figure: Experimental vs. calculated n with cross-validation method (BPANN) for the training set of 53 polyamides and the test set of 14 polyamides. Figure Experimental vs. calculated n with cross-validation method (BPANN) for the training set of 53 polyamides and the test set of 14 polyamides

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