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Designing High-Refractive Index Polymers Using Materials Informatics

Department of Chemistry, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
Authors to whom correspondence should be addressed.
This article is dedicated to the memory of my friend and esteemed colleague, Professor Bjørn Kåre Alsberg, who passed away shortly after submission. His enthusiasm and optimism will be sadly missed.
Polymers 2018, 10(1), 103;
Received: 4 December 2017 / Accepted: 17 January 2018 / Published: 22 January 2018
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. View Full-Text
Keywords: computer-aided design; high refractive index polymer; QSPR; machine learning computer-aided design; high refractive index polymer; QSPR; machine learning
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Venkatraman, V.; Alsberg, B.K. Designing High-Refractive Index Polymers Using Materials Informatics. Polymers 2018, 10, 103.

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