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<p>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.</p>

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Efficient Prediction of Refractive Index And Abbe Number in Polymers Using Density Functional Theory

  • 期刊论文

  • 发表日期：

    2025年6月4日

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  • Lu-Kun Feng,
  • Ai-Wei Zhang,
  • Guo-Hua Huang,
  • Cai-Zhen Zhu,
  • Ming-Liang Wang,
  • Jian Xu

摘要

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.