Skip to Content
Uncategorized

Solving the Puzzle of Triangular Snowflakes

Snowflakes ought to be hexagonal. So why are triangular ones so often observed?

The beautiful six-fold symmetry of snowflakes is the result of the hydrogen bonds that water molecules form when they freeze.

But snowflakes can form other shapes too when the growth of the crystal is perturbed on one side. In theory, diamonds, trapezoids and other irregular shapes can all occur. And yet the one most commonly observed (after hexagons) is the triangle. The puzzle for is why? What process causes deformed snowflakes to become triangles rather than say squares or rectangles?

Today, Kenneth Libbrecht at the California Institute of Technology in Pasadena provides an answer. Libbrecht, you may remember, has built an amazing snowflake machine to study the formation of these remarkable tiny crystals.

Their growth and shape, he says, is governed by two processes: the diffusion of water molecules through the air and the molecular dynamics on the surface of the crystal and it is the former that explains triangular crystal formation.

Libbrecht says various phenomena influence the way water molecules get to the surface of a snow crystal but perhaps the most important is aerodynamics. This orients the crystals and ventillates it, determin the rate at which it can grow.

Libbrecht has calculated how small perturbations in the growth rate on one side of a crystal change its shape. He says that whatever the cause of the perturbation, a hexagonal crystal will always continues to change shape in one way or another.

But the curious property of triangular crystals is that they are stable against this kind of change. So when a crystal has become triangular, other perturbations cannot change its shape further.

Triangles are a kind of valley in the energy landscape of snowflake morphology. And this explains why they are so common. Cool!

Keep an eye out for one next time you’re out in the snow.

Ref: arxiv.org/abs/0911.4733: Aerodynamical Effects in Snow Crystal Growth

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

The problem with plug-in hybrids? Their drivers.

Plug-in hybrids are often sold as a transition to EVs, but new data from Europe shows we’re still underestimating the emissions they produce.

How scientists traced a mysterious covid case back to six toilets

When wastewater surveillance turns into a hunt for a single infected individual, the ethics get tricky.

Google DeepMind’s new generative model makes Super Mario–like games from scratch

Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.