A machine learning-based method to design modular metamaterials

Lingling Wu, Lei Liu, Yong Wang, Zirui Zhai, Houlong Zhuang, Deepakshyam Krishnaraju, Qianxuan Wang, Hanqing Jiang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The concept of modular metamaterials and a machine learning-based method are introduced in this Letter. The method starts from selection of the structural bases based on the existing studies and then combines performance evaluation together with structural evolution to construct meta-atoms with specified properties. Both genetic algorithm and neural networks model are adopted to executed the designing process. Mechanical metamaterials with maximized bandgap and tunable bandgaps are demonstrated using the proposed method. This approach offers an effective means to design metamaterials. It is believed that the modular design of metamaterials based on machine learning is capable to construct meta-atoms with specific properties for metamaterials in various fields.

Original languageEnglish (US)
Article number100657
JournalExtreme Mechanics Letters
Volume36
DOIs
StatePublished - Apr 2020

Keywords

  • Machine learning
  • Mechanical metamaterials
  • Modular metamaterials
  • Structural evolution

ASJC Scopus subject areas

  • Bioengineering
  • Chemical Engineering (miscellaneous)
  • Engineering (miscellaneous)
  • Mechanics of Materials
  • Mechanical Engineering

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