Table 3 The AI algorithms used for the prediction of mechanical metamaterials properties and their inverse design
AI algorithms | Advantages | Disadvantages | Prediction | Inverse design | ||
---|---|---|---|---|---|---|
Type | Ref. | Type | Ref. | |||
Artificial neural networks | • Flexibility and scalability • Computationally efficient • Parallel processing • Powerful ability to extract features in data | • Accuracy relies on amount of data • Prone to overfitting • Black box nature | Nonlinear mechanical metamaterials and fractal metamaterials | Inflatable soft membranes | ||
Deep learninga | • Powerful ability to extract features in data • Handling complex Data • Parallel processing • Flexibility and scalability | • Computationally intensive • Accuracy relies on large amount of image data • Prone to overfitting • Black box nature | Copper spheres embedded in polylactide matrices | 2D and elastic mechanical metamaterials | ||
Tetra-chiral auxetics, cellular metamaterials | Gradient mechanical metamaterials | |||||
Magneto-mechanical metamaterials and auxetic kirigami metamaterials | Metasurfaces, magneto-mechanical metamaterials and auxetic mechanical metamaterials | |||||
Evolutionary strategyb | • Exceling at global optimization • Scalability and invariance • Built-in feature selection • High interpretability • Robustness to noise • Less susceptible to overfitting | • Computationally intensive • Fixed standard deviation parameter of noise • Slow search speed | Nanoscale corrugated plates | 2D and 3D mechanical metamaterial with nonlinear response, fractal metamaterials | ||
Genetic programming | • Global search ability • Scalability • Simple process • Built-in feature selection • High interpretability • Robustness to noise | • Computationally intensive • Complicated programming implementation • Slow search speed | Graphene origami metamaterials | Auxetic mechanical metamaterials with zero Poisson’s ratio | ||
Bayesian network classifiers | • High learning efficiency • Small time and space overhead in classification | • Computational complexity • Dimensional challenges in computing probability • Low interpretability | -- | -- | Mechanical metamaterials with negative stiffness | |
Decision trees | • Simple data preparation • High interpretability • High efficiency and accuracy | • Prone to overfitting • Bias toward features with more levels • Difficulty in handling missing data | Non-rigid square-twist origami | -- | -- |