Multiobjective Design Optimization of Ultra-Thin Electromagnetic Absorbers: High-Throughput Screening and Design Frontier Analysis of Massive Candidate Libraries

Abstract

The rapid expansion of high-frequency radar systems and satellite communications demands a new generation of electromagnetic (EM) absorbers that break the traditional trade-off between attenuation efficiency and physical thickness. Conventional ferrite-based composites often require prohibitive thicknesses ( ) to achieve impedance matching at lower frequencies, limiting their applicability in weight-sensitive aerospace platforms. This study presents a rigorous multi-objective optimization framework applied to a massive library of 51,182 potential candidates, leveraging high-throughput computational screening and machine learning to identify ultra-thin, high-bandwidth absorbers. By employing Pareto frontier analysis, we isolate a discrete set of "best-in-class" materials capable of achieving a Reflection Loss (RL) exceeding  at thicknesses as low as , effectively circumventing Snoek's limit constraints. Beyond simple performance metrics, we introduce a comprehensive robustness analysis, verifying that top-tier candidates maintain their absorption efficacy under simulated manufacturing tolerances (  lattice strain). Furthermore, ternary stoichiometric mapping reveals a critical design rule: optimal broadband performance ( ) is driven not by maximizing magnetic content alone, but by a synergistic balance of magnetic, dielectric, and conductive phases. The result is a validated, actionable Selection Logic Tree that translates complex informatics data into clear synthesis criteria, providing a roadmap for the experimental realization of next-generation low-observable materials.