Data-efficient design of DOPO/rare-earth flame-retardant epoxy resins for carbon fiber composites

To address the challenges of data sparsity and model generalization in small-sample material optimization for flame-retardant resin matrices in composites, this study proposes a “parameterization-visualization” dual-driven strategy. This approach establishes an efficient, standardized pathway for AI-driven material development without relying on black-box algorithms. First, bivariate experimental data involving 10-(2,5-dihydroxyphenyl)-10H-9-oxa-10-phospha-phenanthrene-10-oxide (DOPO-HQ) and a self-developed R-powder (composed of Y₂O₃-stabilized borophosphate glass, zinc borate, and SiO₂) were utilized to generate a high-resolution performance matrix via contour interpolation, thereby transforming sparse experimental points into continuous performance surfaces. Second, a customizable weighted total scoring function was introduced to map multi-objective optimization onto a visualized, single-objective comprehensive scoring field. This not only enabled the precise identification of compliant variable intervals and the global optimum—converting discrete data into a continuous parameter space—but also provided structured training datasets for future machine learning models. Guided by this strategy and leveraging the core AI model of Surrogate-Based Optimization (SBO), the optimal 48.3D/63.9R formulation was rapidly identified with only a limited number of trials. Driven by a synergistic dual-mode mechanism—gas-phase radical quenching by 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) and condensed-phase ceramic glaze formation by molten R-powder—this system achieved superior processability and mechanical integrity while meeting the impregnation and curing demands of large-tow carbon fiber composites. Beyond demonstrating exceptional gas-phase/condensed-phase synergistic flame retardancy, this work validates the proposed parameterized optimization path as a critical bridge connecting small-sample experiments with future large-scale AI optimization. It offers a novel methodological paradigm for accelerating the intelligent design of high-performance flame-retardant resins tailored for large-tow composite materials, effectively overcoming current limitations in data scarcity and model adaptability.