The rapid and accurate prediction of self-diffusion coefficients for pure fluids and mutual diffusion coefficients for binary mixtures remains a focal point of current research. In this work, molecular descriptors are innovatively defined by introducing molecular repulsive and attractive pressures based on the Carnahan-Starling equation of state. This approach aims to improve the quantitative structure–property relationship (QSPR) models for predicting thermophysical properties that vary with thermodynamic conditions. The shapely additive explanation method is employed for a comprehensive analysis of the model mechanism. Among the six molecular descriptors selected, our defined attractive pressure (patt), MATS2e and SpMin3_Bh(v) contribute significantly to the self-diffusion coefficient QSPR model. Based on this approach, QSPR models are constructed for 15 hydrocarbons and five of their binary mixtures. The results demonstrate that the GA-BPNN-based QSPR model for pure fluids achieves high prediction accuracy, with an external Rext2 of 0.978 and an external RMSEext of 5.349 × 10−9·m2·s−1. The optimal mixture descriptor type is x1d1+x2d2, and the GS-SVM-based mixture QSPR model predicts 98.24 % of values within a 5 % error margin, with Rext2 of 0.991 and RMSEext of 0.042 × 10−9·m2·s−1.
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