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(...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 LorentzLorenz 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.展开更多
基金financially supported by the Shenzhen Science and Technology Project(Nos.JCYJ20210324095210028,JSGGZD20220822095201003)the National Natural Science Foundation of China(U21A2087)。
文摘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 LorentzLorenz 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.
