<scp>AI</scp> ‐Assisted Design of Halogen‐Free <scp>EVA</scp> /Mg( <scp>OH</scp> )2– <scp>CNT</scp> / <scp>GNP</scp> Nanocomposites for Enhanced Electrical, Thermal, and Flame‐Retardant Performance

ABSTRACT Flexible and wearable electronics require polymer composites that unite electrical conductivity, thermal management, mechanical compliance, and inherent flame retardancy. Conventional halogen‐free EVA systems often optimize a single property and rely on qualitative dispersion assessment, limiting reproducibility and low‐loading multifunctionality. Here, EVA/Mg(OH) 2 nanocomposites reinforced with hybrid multi‐walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) were evaluated using an AI‐assisted FESEM image‐segmentation framework. Dispersion was quantified via network connectivity, inter‐particle distance, and agglomeration index, and these metrics correlated strongly with electrical and thermal transport. The optimized 5 wt% hybrid formulation achieved σ = 1.4 × 10 −2 S m −1 , k = 0.62 W m −1 K −1 , EMI shielding effectiveness &gt; 20 dB in the X‐band (8–12 GHz), and UL 94 V‐0 classification, while retaining flexibility. The electrical percolation threshold (~4 wt%) was markedly lower than many EVA benchmarks, confirming the efficiency of the 1D–2D hybrid network. By advancing dispersion analysis from qualitative observation to a quantifiable design variable, this work establishes a data‐driven pathway for scalable, halogen‐free nanocomposites. The results highlight routes to lightweight, multifunctional materials for flexible electronics, automotive electronics, and aerospace thermal management, underscoring the role of AI‐enabled analysis in polymer‐engineering design.