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Designing High-Refractive Index Polymers Using Materials Informatics

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Publisher MDPI
Date 2019 Apr 11
PMID 30966141
Citations 4
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Abstract

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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References
1.
Viera A, Garrett J . Understanding interobserver agreement: the kappa statistic. Fam Med. 2005; 37(5):360-3. View

2.
Lameijer E, Kok J, Back T, Ijzerman A . The molecule evoluator. An interactive evolutionary algorithm for the design of drug-like molecules. J Chem Inf Model. 2006; 46(2):545-52. DOI: 10.1021/ci050369d. View

3.
Balazs A, Emrick T, Russell T . Nanoparticle polymer composites: where two small worlds meet. Science. 2006; 314(5802):1107-10. DOI: 10.1126/science.1130557. View

4.
Yu X, Yi B, Wang X . Prediction of refractive index of vinyl polymers by using density functional theory. J Comput Chem. 2007; 28(14):2336-41. DOI: 10.1002/jcc.20752. View

5.
De Vleeschouwer F, Van Speybroeck V, Waroquier M, Geerlings P, De Proft F . Electrophilicity and nucleophilicity index for radicals. Org Lett. 2007; 9(14):2721-4. DOI: 10.1021/ol071038k. View