Accelerated Design of Flame Retardant Polymeric Nanocomposites via Machine Learning Prediction

Improving the flame retardancy of polymeric materials used in engineering applications is an increasingly important strategy for limiting fire hazards. However, the wide variety of flame retardant polymeric nanocomposite compositions prevents quick identification of the optimal design for a specific application. In this study, we built a flame retardancy database of more than 800 polymeric nanocomposites, including information from polymer flammability, thermal stability, and nanofiller properties. Then, we applied five machine learning algorithms to predict the flame retardancy index for different types of flame retardant polymeric nanocomposites. Among them, extreme gradient boosting regression gives the best prediction with a coefficient of determination (R2) of 0.94 and a root-mean-square error of 0.17. In addition, we studied how the physical features of polymeric nanocomposites affected flame retardancy using the correlation matrix and feature importance plot, which in turn was used to guide the design of polymeric nanocomposites for flame retardant applications. Following the guidelines, a high-performance flame retardant polymeric nanocomposite was designed and synthesized, and the experimental FRI result was compared with the machine learning prediction (6% prediction error). This result demonstrated a fast identification of flame retardancy of polymeric nanocomposite without large-scale fire tests, which could accelerate the design of functional polymeric nanocomposites in the flame retardant field.