Prediction of self-diffusion coefficients of ionic liquids using back-propagation neural networks

被引：0

作者：

Xiao Y. ^{[1
]}

Shi Z. ^{[1
]}

Wan R. ^{[1
]}

Song F. ^{[1
]}

Peng C. ^{[1
]}

Liu H. ^{[1
]}

机构：

[1] School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai

来源：

Huagong Xuebao/CIESC Journal | 2024年 / 75卷 / 02期

关键词：

ionic liquids; neural networks; prediction; QSPR; self-diffusion coefficient;

D O I：

10.11949/0438-1157.20230955

中图分类号：

学科分类号：

摘要：

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. © 2024 Materials China. All rights reserved.

引用

页码：429 / 438

页数：9

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