Metamaterial created with Artificial Intelligence that transforms a brittle material into a sponge-like material. Unlike a sponge, this metamaterial is stiff until a critical force is reached after which it becomes easily compressible.
Machine learning
‘We follow a computational data-driven approach for exploring a new metamaterial concept and adapting it to different target properties, choice of base materials, length-scales, and manufacturing processes.’ Guided by machine learning, Bessa fabricated two designs at different length scales that transform brittle polymers into lightweight, recoverable and super-compressible metamaterials. The macro-scale design is tuned for maximum compressibility, while the micro-scale is designed for high strength and stiffness.
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Untapped regions of the design space
Yet, Bessa argues that the most important aspect of the work is not the particular material that was created but the ability to reach untapped regions of the design space via machine learning. ‘The important thing is that machine learning creates an opportunity to invert the design process by shifting from experimentally-guided investigations to computationally data-driven ones, even if the computer models are missing some information. The essential requisites are that ‘enough’ data about the problem of interest is available, and that the data is sufficiently accurate.’ Bessa is a strong proponent of data-driven research in mechanics and materials science. ‘Data-driven science will revolutionize the way we reach new discoveries, and I can’t wait to see what the future will bring us.’
Publication: Advanced Materials (2019)
Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible
https://onlinelibrary.wiley.com/doi/full/10.1002/adma.201904845
Source: TU Delft
TU Delft researchers design new material by using Artificial Intelligence only
https://www.tudelft.nl/en/2019/tu-delft/tu-delft-researchers-design-new-material-by-using-artificial-intelligence-only/
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