

FOLLOWUS
College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen 518055, China
czzhu@szu.edu.cn (C.Z.Z.)
wangml@szu.edu.cn (M.L.W.)
Received:13 February 2025,
Revised:24 March 2025,
Accepted:08 April 2025,
Published Online:04 June 2025,
Published:01 August 2025
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Feng, L. K.; Zhang, A. W.; Huang, G. H.; Zhu, C. Z.; Wang, M. L.; Xu, J. Efficient prediction of refractive index and abbe number in polymers using density functional theory. Chinese J. Polym. Sci. 2025, 43, 1468–1482
Lu-Kun Feng, Ai-Wei Zhang, Guo-Hua Huang, et al. Efficient Prediction of Refractive Index and Abbe Number in Polymers Using Density Functional Theory[J]. Chinese journal of polymer science, 2025, 43(8): 1468-1482.
Feng, L. K.; Zhang, A. W.; Huang, G. H.; Zhu, C. Z.; Wang, M. L.; Xu, J. Efficient prediction of refractive index and abbe number in polymers using density functional theory. Chinese J. Polym. Sci. 2025, 43, 1468–1482 DOI: 10.1007/s10118-025-3353-3.
Lu-Kun Feng, Ai-Wei Zhang, Guo-Hua Huang, et al. Efficient Prediction of Refractive Index and Abbe Number in Polymers Using Density Functional Theory[J]. Chinese journal of polymer science, 2025, 43(8): 1468-1482. DOI: 10.1007/s10118-025-3353-3.
English Summary: This study employs coupled perturbed Density Functional Theory (DFT) to efficiently predict polymer refractive indices (RI) and Abbe numbers. By calculating frequency-dependent polarizability and molecular volume
and applying linear corrections based on polymer classes
the method achieves high correlation with experimental data.
Polymer optical materials are becoming increasingly important in modern technologies owing to their unique properties. This study applies coupled perturbed density functional theory (DFT) to predict the refractive index (RI) and Abbe number of polymers. Using the Lorentz-Lorenz equation
the frequency-dependent polarizability and molecular volume were calculated to estimate RI. Wavelength-dependent RI values were used to derive the Abbe numbers. Our results show a strong correlation with experimental data
with Pearson coefficients of 0.912 for RI and 0.968 for Abbe number
enabling the introduction of linear correction functions to minimize discrepancies between theoretical predictions and experimental results. By categorizing polymers into classes such as poly(methyl methacrylate) (PMMA)-
polyethylene (PE)-
polycarbonate (PC)-
polyimide (PI)-
and polyurethane (PU)-based materials
this method enables precise predictions and reduces discrepancies using linear correction functions. This efficient and direct computational framework avoids the complexity of traditional models and offers a practical tool for the design and optimization of advanced optical materials.
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