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He et al. Microstructures 2023;3:2023037 https://dx.doi.org/10.20517/microstructures.2023.29 Page 17 of 24
1D/3D MLP Realize accurate prediction of variable thickness curved beams and 2020
structure their properties. Efficient and accurate optimization design results
[138]
were obtained with different optimization objectives .

GAN Generate lightweight and high load-bearing performance lattice 2021
structures using GAN and conduct experimental verification [139] .

Figure 4. ML for the design of static characteristics in mechanical meta-structures. (A) Searching for the graphene kirigami with the
[132]
best stretching performance through gradual training of CNN . Reproduced with the permission of Ref. [132] Copyright 2018, the
American Physical Society. (B) Combining CNN and GA to realize lattice metamaterial design satisfying additive manufacturing
[136] [138]
constraints . (C) Design of curved beams with best mechanical properties based on MLP and optimization methods . Reproduced
with the permission of Ref. [138] Copyright 2020, Elsevier. (D) Design of lightweight lattice structures by GAN-based inverse design
[139]
framework .
cutting position control the elastic stretchability of graphene kirigami. They first trained CNN through
supervision to predict the stretchability of graphene kirigami expressed by yield strain. Then, in the inverse
design, the CNN is trained using the dataset obtained from molecular dynamics calculations, and the model
[133]
is gradually trained using the best performance predicted by the CNN. In subsequent research , they
proposed a supervised AE to design graphene kirigami. Kollmann et al. reported the 2D metamaterial