Developing Quantitative Structure–Property Relationship Models To Predict the Upper Flammability Limit Using Machine Learning

In
this study, machine learning algorithms, such as support vector
machine (SVM), k-nearest-neighbors (KNN), and rndom forest (RF), are
applied to improve the accuracy of the quantitative structure–property
relationship (QSPR) models to predict the upper flammability limit
(UFL) of pure organic compounds. Ten molecular descriptors are utilized
to develop the QSPR model. The experimental data set contains 79 chemicals
and is split into 70% training and 30% test set in order to conduct
cross-validation. The multiple linear regression (MLR) QSPR model
of denary logarithms of the UFL obtained in this study has six molecular
descriptors and an overall root-mean-square error (RMSE) of 0.145.
The other four descriptors are eliminated based on statistical insignificance.
The QSPR models aided by SVM and RF improve the prediction of the
UFL as indicated by their overall RMSEs of 0.118 and 0.095, respectively.
However, the QSPR model aided by KNN demonstrated the least performance
with the overall RMSE of 0.163.