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- W4385647174 abstract "The critical temperature is an important parameter in the design and selection of binary organic mixtures. Rapid and accurate prediction has been a focus of research. Strong nonlinear relationships exist between molecular characteristics and critical temperature. Developing nonlinear models is an important measure to improve the prediction accuracy. In this paper, 56 different QSPR models are developed to predict the critical temperature using 14 types of mixture descriptors and four modeling methods (MLR, XGBoost, RBFNN and SVM). A dataset containing 2540 data points is adopted. Results show x1d1+x2d2 is the optimum mixture descriptor type, and the accuracy of the nonlinear models is better than that of the linear models. The model combining x1d1+x2d2 and SVM exhibits the best predictive power; the Rext2, RMSEext, and AARD are 0.988, 10.057 K, and 1.27% respectively. For this model, 78.98% of the data have an absolute deviation of less than 5 K, and the accuracy is better than that of existing QSPR models for critical temperature of mixtures. The application domain analysis shows the model has good performance for novel binary organic mixtures. In addition, compared with the empirical calculation methods for predicting the critical temperature, the results show the developed model has higher reliability." @default.
- W4385647174 created "2023-08-09" @default.
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- W4385647174 date "2023-12-01" @default.
- W4385647174 modified "2023-10-17" @default.
- W4385647174 title "New QSPR models for predicting critical temperature of binary organic mixtures using linear and nonlinear methods" @default.
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- W4385647174 doi "https://doi.org/10.1016/j.fluid.2023.113916" @default.
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