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Area of Science:

  • Materials Science
  • Electromagnetics
  • Artificial Intelligence

Background:

  • Developing high-performance microwave absorbers with ultrathin profiles and tunable bandwidth is challenging for electromagnetic stealth.
  • Traditional methods for absorber design, especially for magnetic absorbers, often involve inefficient trial-and-error due to coupled permittivity and permeability.

Purpose of the Study:

  • To develop a neural network-based strategy for permittivity engineering in microwave absorbers.
  • To decouple interdependent electromagnetic parameters using a novel "permeability locking-permittivity optimization" paradigm.
  • To enable the inverse design and guided synthesis of advanced microwave absorption materials.

Main Methods:

  • Constructed a high-throughput permittivity feature space using tensor-based electromagnetic theory calculations.
  • Implemented a dual-task screening strategy for identifying optimal absorption conditions.
  • Utilized an AI-guided framework for inverse design and material synthesis.

Main Results:

  • Successfully synthesized flaky carbonyl iron/barium titanate composites.
  • Achieved an effective absorption bandwidth of 5.1 GHz at an ultralow thickness of 1.0 mm.
  • Demonstrated optimal reflection loss of -45.12 dB at 1.9 mm and enhanced corrosion resistance due to a protective Si─O─Si surface layer.

Conclusions:

  • Established an AI-guided paradigm bridging electromagnetic theory and materials design for accelerated development.
  • The developed framework offers a robust and generalizable platform for advanced microwave absorber design.
  • The synthesized material exhibits excellent performance and durability for practical applications.