(724g) Computational and Data-Driven Discovery of Novel High Refractive Index Polymers

Organic materials with refractive index (RI) values higher than 1.7 have attracted significant interest in recent years due to the tremendous potential for their application in optical and optoelectronic devices. According to the Lorentz-Lorenz equation, the incorporation of substituents with a high molar refractivity and low molar volume can increase the RI values of these materials. Polymers with a compact structure (such as aromatic polyimides and poly(aryleneethynylene)s) are based on low molar volume building blocks, and are thus generally promising candidates for high-RI materials. However, their molar refractivity has to be maximized as well in order to obtain a viable HRI material. The molar refractivity depends on the polarizability of the elements or moieties comprising the compound of interest. As carbon has only a moderate atomic polarizability, typical carbon-based polymers have RI values of around 1.5. Highly polarizable elements (such as sulfur, phosphorus, bromine, iodine, etc.) can significantly improve the RI values of the corresponding polymers. Hydrogen and strongly electronegative elements (such as oxygen and fluorine) have very low polarizability and exhibit the inverse effect.

We present a computational study of novel high-RI polymer candidates proposed according to the guidelines discussed above. Various paths to introducing highly polarizable elements into carbon-based frameworks are being explored. We compute static polarizabilities at the density functional theory (DFT) level and benchmark the predictive performance of different model chemistries and computational protocols. We obtain RI values based on the Lorentz-Lorenz equation.

To accelerate the discovery process of next-generation high-RI materials, we employ a high-throughput screening, materials informatics, and data-driven rational design approach that we have been developing in the group. It is a powerful tool and has shown to be highly promising for rapidly identifying polymer candidates with exceptional RI values.