4. Conclusions
Two f (T ,P ,I )-QSPR models based on 19335ρ data points and 9238 η data points were established to calculate the ρ and η of ILs under variable temperature and pressure. In order to accurately verify the stability as well as the robustness of the f (T ,P ,I )-QSPR model, a new internal validation method, LOIO-CV, was proposed, which has more strict evaluation criteria and is more reliable compared with the traditional LOO-CV. Moreover, the stability of thef (T ,P ,I )-QSPR model is improved by using data pre-screening to equalize the distribution of data points of ILs and introducing descriptors to the temperature and pressure terms. Furthermore, using only atomic linkage relationships of ILs, no additional time is required to obtain the optimal structure of the ILs. The results of statistical parameter analysis show that these models have good prediction accuracy and reliability with highR 2 and low MAE. In the evaluation of ILsf (T ,P ,I )-QSPR model, both models passed the rigorous LOIO-CV, which indicates that thesef (T ,P ,I )-QSPR models have good stability, robustness, and relatively accurate prediction performance. Meanwhile, we also found that the evaluation results of LOIO-CV were affected by the distribution of ion species, that is, the model established by the dataset with a more balanced distribution of data points of different ILs had higher stability and robustness. Therefore, the equilibrium distribution of different ILs data points is particularly important when modeling the properties of ILs by using QSPR method. In one sense, two proposed f (T ,P ,I )-QSPR models are widely applicable to predict the ρ and η properties of ILs, and these models provide an intelligent tool for predicting the design or synthesis of ILs containing novel cations and anions. It is worth mentioning that the strategy can be widely applied to the estimation of other properties of ILs, such as environmental toxicity and other related physicochemical properties.