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Data-Driven Design for Metamaterials and Multiscale Systems: A Review
Doksoo Lee1, Wei Wayne Chen2, Liwei Wang1
1Dept. of Mechanical Engineering, Northwestern University, Evanston, IL, 60208, USA.
Advanced Materials (Deerfield Beach, Fla.)
|December 5, 2023
Summary
Data-driven design unlocks the potential of metamaterials, artificial materials with unique properties. This review explores methods for data acquisition, unit cell design, and multiscale optimization in metamaterial development.
Area of Science:
- Materials Science
- Engineering
- Artificial Intelligence
Background:
- Metamaterials are engineered materials with properties beyond natural limits.
- Their complex design space and structure-property relationships present significant challenges.
- Data-driven design offers a promising paradigm for advancing metamaterial development.
Purpose of the Study:
- To provide a comprehensive overview of data-driven design methodologies for metamaterials.
- To organize and analyze existing research into distinct data-driven modules.
- To identify future research opportunities in the field.
Main Methods:
- Data acquisition strategies for metamaterial research.
- Machine learning applications in designing metamaterial unit cells.
- Data-driven approaches for multiscale optimization of metamaterials.
Main Results:
- Categorization of data-driven approaches based on shared principles.
- Analysis of the strengths and applicability of different methods.
- Exploration of interconnections between data acquisition, unit cell design, and multiscale optimization.
Conclusions:
- Data-driven design is crucial for realizing the full potential of metamaterials.
- Understanding the methodology across modules is key for future advancements.
- Open research questions and opportunities exist in optimizing metamaterial design.
