Ann Arbor Times

A recent study from the University of Michigan has provided new insights into quasicrystals, a type of solid that occupies an intermediate state between crystals and glass. The research presents the first quantum-mechanical simulations of quasicrystals, revealing their stability as materials despite their non-repeating atomic patterns.

Quasicrystals were initially thought to be impossible until Israeli scientist Daniel Shechtman discovered them in 1984 while working with aluminum and manganese alloys. His discovery revealed atoms arranged in an icosahedral structure with five-fold symmetry, challenging existing beliefs about crystal structures. This work eventually earned Shechtman the Nobel Prize in Chemistry in 2011.

The study, led by Wenhao Sun, the Dow Early Career Assistant Professor of Materials Science and Engineering at the University of Michigan, addresses long-standing questions about how quasicrystals form. "We need to know how to arrange atoms into specific structures if we want to design materials with desired properties," said Sun. "Quasicrystals have forced us to rethink how and why certain materials can form."

Woohyeon Baek, a doctoral student at U-M and the study's first author, highlighted the challenges scientists faced in understanding quasicrystal stability due to their lack of repeating patterns. Traditional methods like density functional theory rely on infinite sequences for calculating stability.

The researchers developed a method involving smaller nanoparticles extracted from larger simulated blocks of quasicrystal. By calculating energy levels within these nanoparticles, they determined that two well-studied quasicrystals—one an alloy of scandium and zinc, another of ytterbium and cadmium—are enthalpy-stabilized.

Vikram Gavini, a co-author and professor at U-M, explained improvements made to computational algorithms: "In conventional algorithms, every computer processor needs to communicate with one another, but our algorithm is up to 100 times faster because only the neighboring processors communicate." This advancement enables more efficient simulations across various materials.

The research received funding from the U.S. Department of Energy and utilized computing resources from institutions including the University of Texas and national laboratories such as Lawrence Berkeley and Oak Ridge.