Lower flammability limit (LFL) of hydrocarbon mixture is a critical property for fire and explosion hazards. In this study, by using experimental LFL data of hydrocarbon mixture from a single reference, quantitative structure‐property relationship (QSPR) models have been established using four machine learning methods, namely, k‐nearest neighbors, support vector machine, random forest, and boosting tree. The K‐fold cross‐validation method, which has significant advantages over the traditional validation set approach, is implemented for QSPR model evaluation. Prediction errors and accuracy are assessed and compared with traditional multiple linear regression. The results show that models generated by machine learning methods have a significantly lower root mean square error than traditional methods in both training and test data sets. This is the first time that machine learning‐based QSPR models are developed for prediction of hydrocarbon mixture LFL, and the models are proven to be highly predictable and reliable.

中文翻译：

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使用定量结构-性能关系模型对烃混合物下可燃极限的机器学习预测

烃混合物的可燃性下限 (LFL) 是火灾和爆炸危险的关键特性。在这项研究中，通过使用来自单一参考的烃混合物的实验 LFL 数据，使用四种机器学习方法建立了定量结构性质关系 (QSPR) 模型，即 k-最近邻、支持向量机、随机森林和助推树。K折交叉验证方法比传统的验证集方法具有显着优势，用于QSPR模型评估。评估预测误差和准确性，并与传统的多元线性回归进行比较。结果表明，在训练和测试数据集中，机器学习方法生成的模型的均方根误差明显低于传统方法。