Lower flammability limit (LFL), upper flammability limit (UFL), auto-ignition temperature (AIT) and flash point (FP) are crucial hazardous properties for fire and explosion hazards assessment and consequence analysis. In this study, a comprehensive prediction model set was constructed by using expanded chemical mixture databases of chemical mixture hazardous properties. Machine learning based gradient boosting quantitative structure-property relationship (GB-QSPR) method is implemented for the first time to improve the model performance and prediction accuracy. The result shows that all developed models have significantly higher accuracy than other regular QSPR models, with the 5-fold cross-validation RMSE of LFL, UFL, AIT, and FP models being 1.06, 1.14, 1.08, and 1.17, respectively. All developed QSPR models can be used to estimate reliable chemical mixture hazardous properties and provide useful guidance in chemical mixture hazard assessment and consequence analysis.

燃烧下限（LFL），燃烧上限（UFL），自燃温度（AIT）和闪点（FP）是火灾和爆炸危险评估和后果分析的关键危险特性。在这项研究中，通过使用化学混合物危险特性的扩展化学混合物数据库构建了一个综合的预测模型集。首次实现了基于机器学习的梯度增强定量结构-属性关系（GB-QSPR）方法，以提高模型性能和预测精度。结果表明，所有开发的模型均具有比其他常规QSPR模型更高的准确性，LFL，UFL，AIT和FP模型的5倍交叉验证RMSE分别为1.06、1.14、1.08和1.17。