Optimal descriptors as a tool to predict the thermal decomposition of polymers

Toropova, Alla P.; Toropov, Andrey A.; Kudyshkin, V. O.; Leszczyńska, Danuta; Leszczyński, Jerzy

doi:10.1007/s10910-014-0323-3

Cited by 15 publications

(8 citation statements)

References 17 publications

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“…Using the above described methodology, the T g was modeled based on the dataset of polymers. Similar methodology was used other works . Accordingly, we built 1‐ to 10‐variable models for the training set, which were thereafter applied to the test set.…”

Section: Resultsmentioning

confidence: 99%

“…Similar methodology was used other works. 31,32 Accordingly, we built 1-to 10-variable models for the training set, which were thereafter applied to the test set. The list of models and associated descriptors with each model is provided in Table 2, while in Figure 1, we show plotted graphs of the regression, r 2 , obtained for training and test sets versus number of variables in selected QSPR models for log(T g ).…”

Section: Visualizationmentioning

confidence: 99%

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A computational structure–property relationship study of glass transition temperatures for a diverse set of polymers

Chen

Jabeen

Rasulev

et al. 2018

J Polym Sci B Polym Phys

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The glass transition temperature (Tg) is one of the most important properties affecting the stability of a polymeric material. A cheminformatics‐based approach has been employed to investigate the glass transition temperatures for a set of polymers. Specifically, a set of 80 polymers was used to build a quantitative structure–property relationship (QSAR). By applying a combination of cheminformatics methods, several predictive models were developed consisting of 1–10 physicochemical variables. The best predictive model, which is based on seven descriptors, successfully predicts the glass transition temperatures for the investigated polymers. Furthermore, the best developed model identified several significant descriptors responsible for glass transition temperatures of the investigated polymers with a correlation coefficient of r2 = 0.77. The computational model derived from this study may serve as a powerful tool to predict glass transition temperatures for various polymers. © 2018 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2018, 56, 877–885

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Section: Resultsmentioning

confidence: 99%

Section: Visualizationmentioning

confidence: 99%

A computational structure–property relationship study of glass transition temperatures for a diverse set of polymers

Chen

Jabeen

Rasulev

et al. 2018

J Polym Sci B Polym Phys

View full text Add to dashboard Cite

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“…Although 3D descriptors obtained from tetramers do not capture the realistic structure of large M w polymers, they include very important information about their structure, which allows building more accurate models, as shown previously by Gubskaya et al [ 36 ]. Similarly, other authors had previously used monomers [ 52 ] or less than 5 repeating monomeric units [ 53 ] to obtain molecular descriptors.…”

Section: Methodsmentioning

confidence: 99%

Developing a Suitable Model for Water Uptake for Biodegradable Polymers Using Small Training Sets

Valenzuela

Knight

Kohn

2016

International Journal of Biomaterials

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Prediction of the dynamic properties of water uptake across polymer libraries can accelerate polymer selection for a specific application. We first built semiempirical models using Artificial Neural Networks and all water uptake data, as individual input. These models give very good correlations (R 2 > 0.78 for test set) but very low accuracy on cross-validation sets (less than 19% of experimental points within experimental error). Instead, using consolidated parameters like equilibrium water uptake a good model is obtained (R 2 = 0.78 for test set), with accurate predictions for 50% of tested polymers. The semiempirical model was applied to the 56-polymer library of L-tyrosine-derived polyarylates, identifying groups of polymers that are likely to satisfy design criteria for water uptake. This research demonstrates that a surrogate modeling effort can reduce the number of polymers that must be synthesized and characterized to identify an appropriate polymer that meets certain performance criteria.

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“…In the past, approaches based on evolutionary algorithms have been successfully applied to the design of drug-like molecules [ 18 ], dyes for solar cells [ 15 ] and olefin metathesis catalysts [ 19 ]. In recent years, a number of polymer properties relevant for optoelectronic applications such as the refractive index [ 20 , 21 , 22 ], glass transition temperature [ 23 ] and thermal decomposition temperature [ 24 ] have been modelled using QSPR methods wherein descriptors calculated from the monomer structures have been correlated with the property of interest. Continuing with this approach, we make use of such models to carry out a multiple criteria-based virtual screening of polymers.…”

Section: Introductionmentioning

confidence: 99%

Designing High-Refractive Index Polymers Using Materials Informatics

Venkatraman

Alsberg

2018

Polymers

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A machine learning strategy is presented for the rapid discovery of new polymeric materials satisfying multiple desirable properties. Of particular interest is the design of high refractive index polymers. Our in silico approach employs a series of quantitative structure–property relationship models that facilitate rapid virtual screening of polymers based on relevant properties such as the refractive index, glass transition and thermal decomposition temperatures, and solubility in standard solvents. Exploration of the chemical space is carried out using an evolutionary algorithm that assembles synthetically tractable monomers from a database of existing fragments. Selected monomer structures that were further evaluated using density functional theory calculations agree well with model predictions.

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Optimal descriptors as a tool to predict the thermal decomposition of polymers

Cited by 15 publications

References 17 publications

A computational structure–property relationship study of glass transition temperatures for a diverse set of polymers

A computational structure–property relationship study of glass transition temperatures for a diverse set of polymers

Developing a Suitable Model for Water Uptake for Biodegradable Polymers Using Small Training Sets

Designing High-Refractive Index Polymers Using Materials Informatics

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