A novel approach toward the prediction of the glass transition temperature: Application of the EVM model, a designer QSPR equation for the prediction of acrylate and methacrylate polymers

Camelio, Philippe; Cypcar, Christopher C.; Lazzeri, Véronique; Waegell, B.

doi:10.1002/(sici)1099-0518(19970930)35:13<2579::aid-pola5>3.3.co;2-i

Cited by 17 publications

(45 citation statements)

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“…Many researchers have ever attempted to develop quantitative structure property relationship (QSPR) models for prediction of the T g of polymethacrylates. For example, Camelio et al4 chosen QSPR method involving the EVM (energy, volume, mass) model to predict the T g of acrylate and methacrylate polymers with R 2 = 0.96. Dai et al5 developed a QSPR model with R 2 = 0.980 and s = 8.3 K in terms of three parameters to calculate the T g of 13 polyacrylates and 9 polymethacrylates.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Predicting the Glass Transition Temperature of Polymethacrylates Based on Quantum Chemical Descriptors by Using Hybrid PSO‐SVR

Pei

Cai

Zhu

et al. 2012

Macro Theory & Simulations

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Based on six quantum chemical descriptors (jL-1.356j, E total , qC6, a, q À , and E therm ), the hybrid PSO-SVR is proposed to establish a model for predicting the glass transition temperature (T g ) of 37 polymethacrylates. The prediction performance of SVR was compared with those of reported MLR and ANN models. The results show that the RMSE, MAPE, and R 2 calculated by SVR are superior to those achieved by MLR or ANN model for the identical training set and test set. This investigation reveals that the SVR model is more suitable to be used for prediction of the T g values for unknown polymethacrylates possessing similar structure than the conventional MLR or ANN model, and provides a clue that the method proposed in this study may be useful in computeraided design of new polymethacrylates with desired T g .

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Section: Introductionmentioning

confidence: 99%

Modeling and Predicting the Glass Transition Temperature of Polymethacrylates Based on Quantum Chemical Descriptors by Using Hybrid PSO‐SVR

Pei

Cai

Zhu

et al. 2012

Macro Theory & Simulations

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“…Traditional QSPR approaches take fixed‐size numerical vectors as input, because of their use of standard regression methods (from linear regression to standard neural network). Hence, all molecules must be reduced to numerical vectors of the same dimension by using different a priori encoding approaches, such as, the occurrence of each group in the molecular structure3–5 and different kinds of molecular descriptors 5–11. The need for molecular descriptors restricts the applicability of the method.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, because of computational limitations, the calculations of many kinds of standard descriptors cannot be carried out directly for high‐molecular‐weight molecules. Therefore, polymer descriptors have to be estimated by using approximate procedures or reduced representations (twenty repeating units at best12). These models do not consider the average polymer information, which often heavily affects the target property.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Polymer Properties from their Structure by Recursive Neural Networks

Duce

Micheli

Starita

et al. 2006

Macromol. Rapid Commun.

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Summary: We propose a new approach for predicting polymer properties from structured molecular representations based on recursive neural networks. To this aim, a structured representation is designed for the modeling of polymer structures. This representation can also account for average macromolecule characteristics. Preliminarily, this model is applied to the calculation of the Tg of (meth)acrylic polymers with different stereoregularity.

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“…But he did not use external data set compounds to validate this model. Cameilio correlated the T g values of 50 acrylates and methacrylates and got a relatively good correlation with R 2 = 0.83 5. Katritzky introduced a model with R 2 of 0.928 for 22 medium molecular weight polymers using four parameters 6.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Glass Transition Temperatures for Polystyrenes by a Four‐Descriptors QSPR Model

Wang

et al. 2005

Macro Theory & Simulations

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Summary: A set of new molecular descriptors, RSC, SMC, DHB and MPE, which are obtained directly from polystyrenes monomer structures, are used to predict the Tg values of polystyrenes and generate a QSPR model by multiple linear stepwise regression analysis, with R = 0.959 and s = 15.20 K. Compared with the existing QSPR models, the proposed model requires only four descriptors, which can be obtained by simple calculation and makes it more valuable to predict the Tg values of polymers.

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A novel approach toward the prediction of the glass transition temperature: Application of the EVM model, a designer QSPR equation for the prediction of acrylate and methacrylate polymers

Cited by 17 publications

References 0 publications

Modeling and Predicting the Glass Transition Temperature of Polymethacrylates Based on Quantum Chemical Descriptors by Using Hybrid PSO‐SVR

Modeling and Predicting the Glass Transition Temperature of Polymethacrylates Based on Quantum Chemical Descriptors by Using Hybrid PSO‐SVR

Prediction of Polymer Properties from their Structure by Recursive Neural Networks

Prediction of Glass Transition Temperatures for Polystyrenes by a Four‐Descriptors QSPR Model

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