Enhancing Flame Retardancy in Polypropylene Composites: A Bayesian Optimization Approach

The traditional optimization of intumescent flame-retardant polypropylene (PP) relies on large experimental campaigns that scale poorly with compositional dimensionality, limiting the systematic exploration of tradeoffs between fire performance and material economy. We present a Multi-Objective Bayesian Optimization (MOBO) workflow that couples Gaussian Process (GP) surrogates with the q-Noisy Expected Hypervolume Improvement (qNEHVI) acquisition to co-optimize two competing objectives: maximize the Limiting Oxygen Index (LOI) and minimize total flame-retardant (FR) loading (wt.%). Two practical initialization strategies, Space-Filling Design and literature-guided sampling, are benchmarked, and convergence is monitored via dominated hypervolume and uncertainty calibration. Uniform design-space coverage yields faster hypervolume growth and better-calibrated uncertainty than literature seeding. Under a 20-experiment budget, the best formulation attains an LOI = 27.0 vol.% at 22.74 wt.% FR, corresponding to an estimated 8–14% efficiency gain, defined here as LOI improvement at comparable FR loadings relative to representative baselines. The recovered APP/PER stoichiometric ratios (1.69–2.26) are consistent with established intumescence mechanisms, indicating that a data-driven search can converge to physically meaningful solutions without explicit mechanistic priors. The proposed workflow provides a sample-efficient route to navigate multi-criteria design spaces in flame-retardant PP and is transferable to polymer systems in which performance, cost, and processing constraints must be balanced and exhaustive testing is impractical.