Synopsis

Packing Polyhedra

Physics 7, s24
A computational study determines the maximum packing density of 55,000 different particle shapes, with potential applications in nanotechnology and biology.
E. R. Chen et al., Phys. Rev. X (2014)

Since the 17th Century, when Johannes Kepler first studied how to pack perfect spheres into the tightest possible configuration, mathematicians have analyzed the packing properties of increasingly complex shapes. But this “packing problem” is notoriously difficult, and it is challenging to predict the packing density of one shape even if that of a similar shape is known. Elizabeth Chen at Harvard University, Daphne Klotsa at the University of Michigan, Ann Arbor, and colleagues have now expanded the study of particle packing to 55,000 shapes, enabling a more detailed investigation of how shape affects packing. Reporting in Physical Review X, the team finds that minor shape deformations can have a significant effect on packing density. The result could have applications in nanotechnology and biology, where high packing densities are often required.

Chen, Klotsa, and their colleagues generated the multitude of shapes by using computers to interpolate between symmetric solids such as cubes, tetrahedrons, octahedrons ( 8 faces), dodecahedrons ( 12 faces), and icosahedrons ( 20 faces). Using analytical and computational methods, they investigated the densest possible packing arrangements of each shape in an infinite, periodic three-dimensional box.

A surprising finding of the research is how sensitive the packing density is to certain small changes in shape. For example, truncating the edges of a dodecahedron results in a decreased packing density, but truncating the vertices hardly affects the packing density at all. The team concludes that rather than focus on a specific shape, future studies of packing properties should instead examine families of shapes related by small deformations. – Katherine Kornei


Subject Areas

Computational PhysicsIndustrial Physics

Related Articles

Why Emus Favor Fast Walking
Computational Physics

Why Emus Favor Fast Walking

Emus inherited from their dinosaur ancestors a crouched posture that dictates the gait they adopt when moving quickly, according to a new computer simulation of bird motion. Read More »

Harnessing Machine Learning to Guide Scientific Understanding
Computational Physics

Harnessing Machine Learning to Guide Scientific Understanding

A clever use of machine learning guides researchers to a missing term that’s needed to accurately describe the dynamics of a complex fluid system. Read More »

Time Delays Improve Performance of Certain Neural Networks
Computational Physics

Time Delays Improve Performance of Certain Neural Networks

Both the predictive power and the memory storage capability of an artificial neural network called a reservoir computer increase when time delays are added into how the network processes signals, according to a new model. Read More »

More Articles