Deeper insights into flame retardancy of polymers by interpretable, quantifiable, yet accurate machine-learning model

Abstract

Fire-safety polymer materials are essential in modern society such as electronics, aerospace, new energy. The quantification and prediction of flame retardancy, determined by the chemical composition and the burning process, has always been a bottleneck challenge. Previous empirical design rules and the existing models show large deviations for predicting flame retardancy and are often unexplainable. Here, this study proposes an interpretable model that can quantify the groups contribution of flame-retardancy and predict the flame retardance of intrinsically flame-retardant polymers. The machine learning model simultaneously considers the group structures and their flame-retardant mechanism in both the gas phase and condensed phase, achieving high prediction accuracy (89.8% for the training set and 83.8% for the testing set). It also quantifies the contribution values of various flame-retardant groups (halogen-containing structures, phosphorus-containing structures, phosphorus-nitrogen-containing structures, aromatic ring-containing structures, etc.) in both phases for the first time. The running script that integrates the model has also been open-sourced, providing an emerging strategy for transitioning flame-retardant research from empirical methods to scientific design.