Applying the Monte Carlo technique to build up models of glass transition temperatures of diverse polymers
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ORIGINAL RESEARCH
Applying the Monte Carlo technique to build up models of glass transition temperatures of diverse polymers Andrey A. Toropov 1 & Alla P. Toropova 1
&
Valentin O. Kudyshkin 2 & Nurad I. Bozorov 2 & Sayyora Sh. Rashidova 2
Received: 15 May 2020 / Accepted: 6 July 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Optimal descriptors calculated with SMILES represented a structure of monomer units applied to build up a model of glass transition temperatures of diverse polymers. Quantitative structure-property relationships (QSPRs) were established for the above dataset. The statistical quality of the model of glass transition temperatures is quite good. The simplified molecular input-line entry system (SMILES) has been used to represent the molecular structure of corresponding monomers. The hybrid optimal descriptors calculated with the so-called correlation weights of molecular features extracted from SMILES and molecular hydrogen-suppressed graph (HSG) were used as the basis of the one-variable model. The index of ideality of correlation (IIC) is a new criterion of the predictive potential of the QSPR model. Here, the applicability of the IIC as a tool to improve the predictive potential of the model for glass transition temperatures is confirmed. Keywords Polymer . Glass transition temperature . QSPR . Monte Carlo method . SMILES
Introduction The glass transition temperatures of polymers are important technological characteristics [1] since it corresponds to the upper temperature limit of the heat resistance of plastics. The cost of the experimental determination of this parameter is expensive [2]. In addition, the glass transition temperature can be determined by different methods, but unfortunately, the accuracy of these results can be different. Consequently, the development of mathematical models for the endpoint is a necessary field of computational polymer chemistry.
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11224-020-01588-8) contains supplementary material, which is available to authorized users. * Alla P. Toropova [email protected] 1
Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milan, Italy
2
Institute of Polymer Chemistry and Physics, Academy of Sciences of the Republic of Uzbekistan, Kodyri Street 7b, Tashkent, Uzbekistan 100128
The quantitative structure-property relationships (QSPRs) are a tool to build up predictive models for various endpoints. However, QSPR analysis of polymers remains rare, owing to the specificity of polymer objects (first, molecular size). Nevertheless, QSPR analysis of polymers is possible [1], and factually, it is possible to define hierarchies of the QSPR related to polymers [2]. The index of ideality of correlation (IIC) can be used in both functions as the basis of building up a model and as the criterion of the predictive potential of a model [2]. The basic idea of
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