Synopsis

Machine-Learning Tool Solves Metamaterial Jigsaw

Physics 15, s150
A new tool can determine whether a collection of building blocks will assemble into a mechanically sound structure.
van Mastrigt et al.[1]

Like Lego spaceships or jigsaw puzzles, mechanical metamaterials have properties that emerge from the physical interactions of a collection of building blocks. But unlike Legos and jigsaws, it can be tricky to predict whether a particular set of mechanical building blocks fits together to yield a stable structure. Now Ryan van Mastrigt of the University of Amsterdam and his colleagues demonstrate a machine-learning tool that can do just that [1]. Their tool learns from test structures which blocks squeeze snugly together and then uses that information to make predictions about other potential structures.

The team considered the arrangement of patterned, deformable tiles. These blocks can be rotated into one of four distinct orientations, with each taking on a different pattern if it is subsequently squeezed. The group studied structures made from different combinations of these pattens, using a machine-learning model to find out which ones were gapless and stable.

To train the neural network, van Mastrigt and colleagues started with tile arrangements known to produce both mechanically sound and mechanically unsound structures—including a stable, gapless structure that only appears for a few tile combinations. They fed random building-block arrangements to the neural network to see if it could identify which ones were gapless and mechanically viable. They also set the neural network to analyze the stability of structures in which one individual block was rotated. For all tests, they found that their tool could correctly identify stable structures.

Besides mechanical metamaterials, the team says that the neural network could be used to find functional structures for any system built from blocks, including proteins that fold and chemical compounds that have biological functions.

–Rachel Berkowitz

Rachel Berkowitz is a Corresponding Editor for Physics Magazine based in Vancouver, Canada.

References

  1. R. van Mastrigt et al., “Machine learning of implicit combinatorial rules in mechanical metamaterials,” Phys. Rev. Lett. 129, 198003 (2022).

Subject Areas

Materials Science

Related Articles

A Chiral Crystal’s Orbital Texture
Materials Science

A Chiral Crystal’s Orbital Texture

X-ray experiments reveal that a semimetal exhibits “orbital texture”—an exotic electronic structure resulting in spin-dependent electron transport. Read More »

Electron–Hole System Harbors Rich Phases
Materials Science

Electron–Hole System Harbors Rich Phases

Researchers predict that several exotic states of matter can exist in semiconductor structures hosting electrons in one layer and holes in another. Read More »

Thermal Conductivity Not Too Hot to Handle
Materials Science

Thermal Conductivity Not Too Hot to Handle

A radiometry technique directly measures thermal conductivity in molten metals and confirms the relationship with electrical resistivity. Read More »

More Articles