(342b) Accelerated Design of Flame Retardant Polymeric Nanocomposites Via Machine Learning

Improving the flame retardancy of polymeric materials is an increasingly important strategy to limit the exposure of life safety to more fire hazards, especially in this era when those polymeric materials are widely used almost everywhere in housing, vehicles, and commercial products. However, the wide variety of nanocomposite designs prevent rapid identification of the optimal composition for a given application. In this study, we applied machine learning methods, including k-nearest neighbor (k-NN), support vector regression (SVR), random forest (RF), and gradient boosting regression (GBR), to predict the flame retardancy index (FRI) and related features of cone calorimetry for different types of flame retardant polymeric nanocomposites. The quality of machine learning predictions was evaluated based on the statistical values, such as the coefficient of determination R², root mean square error (RMSE), average relative deviation (ARD), and absolute average relative deviation (AARD). We studied how physical features of polymeric nanocomposites affected flame retardancy using the correlation matrix of each feature, which in turn was used to guide the design of polymeric nanocomposites for flame retardant application. Following the guidelines deduced from these models, a high-efficiency flame retardant polymeric nanocomposites was designed and synthesized, of which the experimental results were compared with the machine learning predictions.