## 2 Basic Analysis of Prediction Models and Model Verification
### Prediction Models of Viscosity and Thermal Conductivity
#### 3.4.1 Prediction Models of Viscosity and Thermal Conductivity
The prediction models of the thermal conductivity of organic substances were classified into five categories by [PERSON] et al. [113]: (1) the group contribution method; (2) empirical formulas based on basic thermophysical properties; (3) the principle of corresponding states; (4) correlations in the data on cognate organic substances; and (5) the molecular dynamics model. The models based on the principle of corresponding states are described above. The correlations in the data on cognate organic substances can also be called the family method, which contains parameters that vary moving from family to family [114]. In the existing literatures, the correlations in the data on cognate organic substances have only been conducted on the data on methane series, ethane series and propane series of halogenated materials, but the electronic fluoride liquids studied in this paper belong to fully or almost fully halogenated hydrocarbons with more than five carbons, which means that the existing correlations in the cognate organic substances are inapplicable for electronic fluoride liquids. In addition, for the molecular dynamics model based on statistical mechanics, it takes more time to model and simulate molecular motion compared with other models. Therefore, the last three categories (4 and (5)) will not be discussed in this section. The model proposed by [PERSON] et al. [113] is based on the group contribution method, as shown in Equations (81) and (82) (hereinafter referred to as the GTC model):  
\[F^{*} =1-0.2756\omega+0.059035\mu_{\rm dim}^{4}+\kappa \tag{70}\]3  
\[F^{*} =1-0.2756\omega+0.059035\mu_{\rm dim}^{4}+\kappa \tag{70}\]4  
where \(TC_{1}\) and \(TC_{2}\) are obtained by the group contribution method and \(Num\) is the number of non-hydrogen atoms. The prediction model of the thermal conductivity proposed by [PERSON] et al. [115] is similar to that of the viscosity. Firstly, the thermal conductivity at the boiling point is obtained by the group contribution method and then, the thermal conductivities at other temperatures are calculated. The model is shown in Equation (83) (hereinafter referred to as the STC model):  
\[F^{*} =1-0.2756\omega+0.059035\mu_{\rm dim}^{4}+\kappa \tag{70}\]5  
where \(\lambda_{b}\) is calculated by the group contribution method, W/(m\(\cdot\)K). The values 0.16 and 0.20 are the suggested values for \(\lambda_{b}\)[115]. [PERSON] et al. [106] compared 20 prediction models of thermal conductivity from the perspective of engineering applications and it is considered that the models proposed by [PERSON] et al. [95,115], [PERSON] et al. [115] and [PERSON] et al. [116] are the best prediction models of thermal conductivity. The prediction model proposed by [PERSON] et al. [115] is shown in Equation (83), while the model by [PERSON] et al. [116] is only suitable for methane series, ethane series and propane series of halogenated materials. [PERSON] et al. proposed that thermal conductivity at the boiling point could be estimated according to \(\lambda_{b}=1.11/\text{M}^{0.5}\) and that liquid thermal conductivity could be predicted by Equation (84) [95,116] (hereinafter referred to as the RSTC model):  
\[F^{*} =1-0.2756\omega+0.059035\mu_{\rm dim}^{4}+\kappa \tag{70}\]6  
A gene expression programming algorithm was used to obtain a prediction model for the representation of liquid-phase thermal conductivity based on approximately 19,000 liquid thermal conductivity data at different temperatures related to 1636 chemical compounds by [PERSON] et al. [117]. This prediction model is shown by Equations (85)-(87) (hereinafterreferred to as the GHTC model).