MIT News breed · Wetenschap

Mathematical framework connects biological principles to manufacturable, adaptive materials

MIT researchers have developed a mathematical framework that translates biological mechanisms into designs for adaptive materials that can be 3D-printed. This system captures how components at different scales in a natural object interact to produce specific behaviors, enabling engineers to create synthetic structures with similar functionalities.

The framework simplifies the design of bioinspired materials by organizing biological behavior into modular building blocks. These blocks can be mathematically validated and then fabricated using 3D printing. This approach, termed 'bio-derivation,' moves beyond simply observing natural behaviors to understanding and translating the underlying mechanisms.

By reducing guesswork in the design process, the framework aims to accelerate the creation of adaptive materials, cut development time, and minimize costs associated with failed prototypes. Potential applications include self-cooling shingles, soft robotic grippers that react to their environment without electronics, and morphing airplane wings.

The system uses category theory to map how stimuli, like humidity, trigger responses across different hierarchical levels within an organism, such as a pine cone. Each level is modeled as a distinct building block, and mathematical rules ensure valid transitions between them. This allows the engineered material to replicate the stimulus-response interactions of the natural system.

The framework extends previous research in hierarchical materials and categorical prototyping. It now closes the loop from multiscale biological mechanics to fabrication specifications and experimentally validated, machine-executable designs. This allows for the explicit transfer of design logic from nature to engineered systems.

The framework's compositional structure enables the recombination of verified building blocks to create novel designs. Researchers demonstrated this by combining elements from a pine cone's humidity-driven bending and a wheat awn's humidity-driven twisting to create a new thermal twisting actuator. This reusability significantly saves computational resources and design effort.

Future plans include applying the framework to more complex biological systems and integrating artificial intelligence to speed up the discovery of new adaptive materials. The researchers envision this as a step towards 'physical AI,' where intelligence can reason about physical mechanisms and translate them into tangible matter, enabling AI to discover, realize, and test new materials.

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