QSPR flash point prediction of binary miscible mixtures: Integrated descriptors and DE-optimized SVR

Experimental determination of flash points (FPs) for liquid mixtures is laborious and costly, highlighting the need for reliable predictive approaches for safety assessment and engineering applications. Although numerous models have been reported for binary miscible mixtures, most rely on fixed model parameters or empirical correlations, which limits their ability to capture the nonlinear relationship between molecular structure and FP. In this study, a quantitative structure-property relationship (QSPR) framework that tightly integrates differential evolution (DE) with support vector regression (SVR) was developed to predict the FP values of binary miscible mixtures, where DE was employed to globally optimize key SVR hyperparameters and enhance model generalization capability. A dataset consisting of 332 compositions from 33 binary mixtures formed by pairwise combinations of 20 pure components was employed, and multiple molecular descriptor representation strategies were deliberately adopted to construct distinct DE-SVR models, enabling a systematic investigation of the combined effects of descriptor representation and model optimization on predictive performance. Three DE-SVR models were established based on different descriptor sets, and their predictive accuracy, robustness, and stability were comprehensively evaluated. Among them, the model constructed using physicochemical parameters exhibited the best overall performance. Comparative analyses with existing FP prediction methods reported in the literature further confirmed the effectiveness and superiority of the proposed DE-SVR-based models. The results of this study provide a practical tool for FP estimation of binary mixtures and offer valuable insights into the joint roles of model optimization and molecular representation in mixture property prediction.