Glasses have disordered arrangements of atoms without the repeating patterns that crystals have. However, there are small-scale patterns of atoms that touch each other that strongly affect the energy of the glass, how the atoms move when they get hot, and other properties like strength and response to an electric field. Unfortunately, there are many possible patterns and many slight variations of each one, so studying them is like sorting the grains of sand on a beach by size and color by hand–it’s an impossible task. Wisconsin MRSEC IRG 1 uses machine learning to sort the sand. They have developed algorithms to find small-scale atomic patterns in large simulations of glasses and link them to the glass’ energy. Ongoing studies have connected patterns to atomic motions, which provides a path to simulations of glasses over long times and low temperatures that are currently impossible.
A team of researchers from the Wisconsin Materials Research Science and Education Center (MRSEC) at the University of Wisconsin–Madison has designed, constructed, and implemented a new, highly specialized piece of research equipment that can be used to visualize the real-time formation and growth of tiny crystals of novel materials. The unique perspective provided by this approach provides access to new ways to discover and develop materials relevant to electronics, optics, and magnetic applications.
Materials with a repetitive pattern the same size as the wavelength of a wave can be used to control the wave, causing it to bend, perfectly reflect or transmit, or even turn around corners. Where different patterns meet, even more exotic behavior occurs, including making highways for light or sound that only travel in one direction or where the waves cannot be dissipated. Synthesizing such materials is a major challenge, which Wisconsin MRSEC researchers have met by adapting a family of 3D printing techniques.