A Quantum‐Mechanical QSAR Model to Predict the Refractive Index of Polymer Matrices

Abstract

Refractive index (RI) is an important optical property for polymer matrices, especially when the color or tint of the cured material is of interest. This is certainly the case for dental restorative applications. In this work, results are presented for a quantitativestructure activity relationship derived from relevant semiempirical quantum mechanical information. This model predicts the RI for a wide variety of polymer matrices using representative structures of polymers, including resin components of several currently used dental restorative materials. The AM1 semiempirical method was used for calculations due to its speed and general reliability. Several structural subunits of the polymer chains were used for the QSAR analysis, but dimer moieties produced the best results for some 60 polymers. The final QSAR model was composed of a multilinear equation that featured the highest occupied molecular orbital -the lowest unoccupied molecular orbital gap and a polarizability index as the two descriptors best able to account for the variation in the data. The final model had R 2 ¼ 0.963, R 2 cv ¼ 0.959, F ¼ 740, and s 2 ¼ 0.0002. Other quality indicators for the correlation and the individual descriptors were within acceptable limits. The presence of electronically related descriptors is encouraging, as these are conceptually tied to the phenomenon of RI. The difference between a theoretically predicted value for poly(propylene oxide) and its monomer was 0.04, as compared to 0.09 from experimental data.

Gas transport and optical properties of ABA-type triblock copolymers designed using fluorine-containing polyimide macroinitiators with methyl methacrylate

“…Oxygen (a) and carbon dioxide (b) permeability as a function of refractive index: blend polymer and triblock copolymer in this study (); other polymers with π electrons (); without π electrons () …”

Prediction of polymer properties using infinite chain descriptors (ICD) and machine learning: Toward optimized dielectric polymeric materials

“…In the past 30 years, numerous attempts have been made to predict polymer properties, where the glass transition temperature may be the most studied one . Studies on the dielectric constant and refractive index have also been reported . Le et al did a comprehensive review on the QSPR modeling for material properties in 2012.…”

“…Even though studies such as the one described in Katrizky's 1996 paper considered the extension of descriptor values for long chains using a numerical treatment, most of the models simply consider a repeat unit with different end‐cap atoms as a complete molecular representation. Monomers with carbons as end‐cap atoms, dimers, and ring‐like dimers have been used as ways of incorporating features of the polymer chemical environment. With these models, two theoretical issues arise: If a descriptor value is an “extensive” property that varies with the scale of the local molecular representation (e.g., the monomer and dimer give different descriptor values), the information contained in this descriptor is contaminated by the choice of the molecular representation.…”

“…1,[4][5][6][7][8][9][10][11][12][13][14][15][16][17] Studies on the dielectric constant and refractive index have also been reported. [16][17][18][19][20][21] Le et al 22 did a comprehensive review on the QSPR modeling for material properties in 2012. Basically, these models can be divided into two categories: group contribution techniques and descriptor-based methods.…”

Quantitative structure‐property relationships studies on free‐radical polymerization chain‐transfer constants for styrene

“…The most commonly used linear method is the stepwise multilinear regression analysis (MLRA), which can run forward or backward. The QSPR approach has been used quite extensively to predict many properties in polymer chemistry and physics, such as refractive index,10–17 glass transition temperature,12, 18–26 lower critical solution temperature,27–30 Flory‐Huggins parameters,31, 32 intrinsic viscosity,33, 34 solubility parameters,35 and monomer reactivity parameters 36–38. However, there have been relatively few attempts to correlate and predict the chain‐transfer constants.…”