@article{17f37d0157e14ffd8b4f0d40db6c78fc,
title = "A machine learning-based method to design modular metamaterials",
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.",
keywords = "Machine learning, Mechanical metamaterials, Modular metamaterials, Structural evolution",
author = "Lingling Wu and Lei Liu and Yong Wang and Zirui Zhai and Houlong Zhuang and Deepakshyam Krishnaraju and Qianxuan Wang and Hanqing Jiang",
note = "Funding Information: LW acknowledges the Foundation for Distinguished Young Talents in Higher Education of Guangdong Province, China under Grant No. 2018KQNCX269 and the Special Funds for the Cultivation of Guangdong College Students{\textquoteright} Scientific and Technological Innovation, China under Grant No. pdjh2020b0598 . YW acknowledges the National Natural Science Foundation of China under Grant Nos. 11872328 , 11532011 and 11621062 , and the Fundamental Research Funds for the Central Universities, China under Grant No. 2018FZA4025 . QW acknowledges the National Key R&D Program of China under Grant No. 2018YFB1201601 . HJ acknowledges the support from the National Science Foundation, USA ( CMMI-1762792 ). Funding Information: LW acknowledges the Foundation for Distinguished Young Talents in Higher Education of Guangdong Province, China under Grant No. 2018KQNCX269 and the Special Funds for the Cultivation of Guangdong College Students? Scientific and Technological Innovation, China under Grant No. pdjh2020b0598. YW acknowledges the National Natural Science Foundation of China under Grant Nos. 11872328, 11532011 and 11621062, and the Fundamental Research Funds for the Central Universities, China under Grant No. 2018FZA4025. QW acknowledges the National Key R&D Program of China under Grant No. 2018YFB1201601. HJ acknowledges the support from the National Science Foundation, USA (CMMI-1762792). Publisher Copyright: {\textcopyright} 2020 Elsevier Ltd",
year = "2020",
month = apr,
doi = "10.1016/j.eml.2020.100657",
language = "English (US)",
volume = "36",
journal = "Extreme Mechanics Letters",
issn = "2352-4316",
publisher = "Elsevier Limited",
}