Accelerated Design of Flame Retardant Polymeric Nanocomposites via Machine Learning Prediction

Abstract

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 (R 2) 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.

Rethinking the pathway to sustainable fire retardants

“…More recently, this technology has been successfully applied to the design of gas-separation membranes, [25,26] polyelemental heterostructures [27] and plastic depolymerization. [28] Although some machine learning algorithms that forecast the flame retardancy of polymers have recently been reported, the prediction models are based on flame retardancy index [29] or limiting oxygen index (LOI) [30] of materials rather than on the molecular design of fire retardants. Herein, we assume that the molecular design of fire retardants could be aided by a machine (or deep) learning model.…”

ROBERT: Bridging the Gap between Machine Learning and Chemistry

“…In particular, the integration of ML in chemistry has opened up new avenues for exploring chemical space and predicting properties of molecules and reaction outcomes. This has led to significant advances in fields such as drug discovery, [1][2][3][4][5] materials science, [6][7][8][9] chemical synthesis, [10][11][12][13][14][15][16][17] and catalyst discovery, [18][19][20][21][22][23][24] among others.…”

Accurate Prediction of Microstructure of Composites using Machine Learning

“…[19] In addition to solely predicting material properties of known composites, ML has also been extensively used in the design and discovery of novel composite materials. [20][21][22][23][24] Gu et al used convolutional neural network in the design of bioinspired hierarchical composites for stronger candidates. [21] Recently, Qiu et al established an ML-assisted composite design framework as an effective and efficient way to find feasible and optimal selections of fiber materials and layup stacking orientation.…”

“…[ 19 ] In addition to solely predicting material properties of known composites, ML has also been extensively used in the design and discovery of novel composite materials. [ 20–24 ] Gu et al. used convolutional neural network in the design of bioinspired hierarchical composites for stronger candidates.…”