QSPR modelling for intrinsic viscosity in polymer–solvent combinations based on density functional theory

ABSTRACT

Linear and nonlinear quantitative structure–property relationship (QSPR) models were developed based on a dataset with 65 polymer-solvent combinations. Seven quantum chemical descriptors, dipole moment, hardness, chemical potential, electrophilicity index, total energy, HOMO and LUMO orbital energies, were calculated with density functional theory at the B3LYP/6-31 G(d) level for polymers and solvents. Considering the strong correlation between intrinsic viscosity and weight, size, shape as well as topological structure of polymers and solvents, topological descriptors were also applied in this work. Meanwhile, the most appropriate polymer structure representation was investigated by considering 1–5 monomeric repeating units. The molecular descriptors were first screened by using the genetic algorithms-multiple linear regression (GA-MLR), with coefficient of determinations ($r^2$) of 0.78 and 0.83 for the training set and the prediction set, respectively. The support vector machine model (SVM) model based on the selected descriptors subset showed a $r^2$ value of 0.95 for the training set and 0.93 for the prediction set. All statistical results suggest that the established QSPR models have good predictability. Furthermore, a new test set obtained from the literature was used for further validation. The $r^2$ values were 0.81 for the MLR model and 0.90 for the SVM model.

KEYWORDS:

• QSPR
• intrinsic viscosity
• polymer solutions
• DFT
• machine learning

Acknowledgements

Financial support from the Fundamental Research Funds for the Central Universities (YJ201838) and the National Natural Science Foundation of China (21776183, 21706220) is gratefully acknowledged.

Disclosure statement

No potential conflict of interest was declared by the author(s).

Supplementary material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2021.1902387

Additional information

Funding

Financial support from the Fundamental Research Funds for the Central Universities (YJ201838) and the National Natural Science Foundation of China (21776183, 21706220).