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Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Physics

Background:

  • Disordered microstructures in natural materials yield unique properties.
  • Understanding microstructure-property relationships is crucial for engineering applications but remains challenging.
  • Metamaterials with complex microstructures often exhibit disorder, complicating their study.

Purpose of the Study:

  • To develop a physics-guided, self-supervised AI framework for designing disordered metamaterials.
  • To establish a method for identifying hidden geometric invariants that govern material properties.
  • To enable the inverse design of novel microstructures with desired physical functions.

Main Methods:

  • A self-supervised artificial intelligence (AI) framework named generative networks for disordered metamaterials (GNDM) was developed.
  • A formula writing module was integrated into neural network training to identify key geometric invariants.
  • The AI framework was trained on a progressively expanding dataset, starting with minimal samples.

Main Results:

  • GNDM successfully identified hidden geometric invariants dictating bulk material properties.
  • The framework manipulated disordered geometric features to design previously unknown structures.
  • Experimental validation confirmed the AI-designed structures' predicted properties.

Conclusions:

  • GNDM provides an integrated AI solution for feature extraction, property prediction, formula writing, and inverse design.
  • The framework elucidates the role of disorder in metamaterials with complex microstructures.
  • This approach accelerates the discovery and engineering of advanced disordered metamaterials.