Designing future‐proof material goes beyond a quest for the best. The next generation of materials needs to be adaptive, multipurpose, and tunable. This is not possible by following the traditional experimentally guided trial‐and‐error process, as this limits the search for untapped regions of the solution space.

This is where artificial intelligence has become a game-changer.

Now, researchers at TU Delft have developed a new super-compressible and strong material without conducting any experimental tests at all, using only AI.

The researchers used a computational data‐driven approach is followed for exploring a new metamaterial concept and adapting it to different target properties, choice of base materials, length scales, and manufacturing processes. Guided by Bayesian machine learning, two designs are fabricated at different length scales that transform brittle polymers into lightweight, recoverable, and super-compressible metamaterials. The macroscale design is tuned for maximum compressibility, achieving strains beyond 94% and recoverable strengths around 0.1 kPa, while the microscale design reaches recoverable strengths beyond 100 kPa and strains around 80%. The data‐driven code is available to facilitate future design and analysis of metamaterials and structures.

“AI gives you a treasure map, and the scientist needs to find the treasure… However, metamaterial design has relied on extensive experimentation and a trial-and-error approach. We argue in favour of inverting the process by using machine learning for exploring new design possibilities while reducing experimentation to an absolute minimum,” said Miguel Bessa, first author of a publication on this subject in Advanced Materials.