Mechanical metamaterials are sophisticated artificial structures with mechanical properties that are driven by their structure, rather than their composition. While these structures have proved to be very promising for the development of new technologies designing them can be both challenging and time-consuming.
Researchers at University of Amsterdam, AMOLF, and Utrecht University have recently demonstrated the potential of convolutional neural networks (CNNs), a class of machine learning algorithms, for designing complex mechanical metamaterials. Their paper, published in Physical Review Letters, specifically introduces two-different CNN-based methods that can derive and capture the subtle combinatorial rules underpinning the design of mechanical metamaterials.
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