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

abstract

  • © 2019 American Chemical Society. 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.

altmetric score

  • 0.25

author list (cited authors)

  • Yuan, S., Jiao, Z., Quddus, N., Kwon, J., & Mashuga, C. V.

citation count

  • 12

publication date

  • February 2019