Quantitative Structure-Property Relationship (QSPR) models for Minimum Ignition Energy (MIE) prediction of combustible dusts using machine learning

Abstract

Quantitative Structure-Property Relationship (QSPR) classification models for Minimum Ignition Energy (MIE) classification of 65 combustible dusts have been developed using machine learning algorithms Random Forests (RF) and Decision Trees (DT). Combustible dusts were categorized based on their MIE values: dusts having MIE > 10 mJ and dusts having MIE < 10 mJ. In this work, an important macroscopic property, dust median diameter (d50) was also used as one of the parameters along with the molecular descriptors for model development. The machine learning Random Forest (RF) algorithm was used for identification of the 13 molecular descriptors having maximum effect on MIE prediction accuracy. Thereafter, these descriptors were used as input parameters to develop binary classification Random Forest (RF) and Decision Tree (DT) models. The RF and DT algorithms displayed good MIE category predictability validated through the test set Receiver Operating Characteristic- Area Under Curve (ROC-AUC) of 0.95 for both the models. Thus, this work has displayed that Random Forests can be used as an important tool for descriptor reduction in QSPR studies and can prove to be a valuable resource for MIE category prediction.