Prediction of self-diffusion coefficients of ionic liquids using back-propagation neural networks
Using the charge density distribution fragment area(Sσ)and hole volume(VCOSMO)obtained by the fragment activity coefficient conductor-like shielding model(COSMO-SAC)as structural descriptors,we developed a quantitative structure-property relationship(QSPR)model,namely the BP-ANN model,to predict cation and anion self-diffusion coefficients of ionic liquids.The range of applicability and predictive capability of the BP-ANN model were also examined and compared with another QSPR model established by linear regression(Model I).The results revealed that the BP-ANN model can be applied to a broader range of ionic liquid species compared with Model I.The BP-ANN model achieves a high coefficient of determination(R2)value exceeding 0.99 in the training,validation,and testing dataset for cations,and surpassing 0.98 for anions across all sub-datasets.For the total dataset,the BP-ANN model yields low average absolute relative deviations(AARD)of 2.8%for cations and 3.7%for anions between calculated and experimental values,while the corresponding values for Model I are 14.54%and 14.57%,respectively.Therefore,the prediction performance of the BP-ANN model is significantly better than that of the model based on linear regression.
万方数据
维普
