A detailed characterization of phases of matter is at the forefront of research in condensed-matter and statistical physics. Although physicists have made incredible progress in the characterization of a wide variety of phases, the identification of novel topological phases remains challenging. Now, Yi Zhang and Eun-Ah Kim from Cornell University, New York , have taken a big-data approach to tackling this problem. In their work, thousands of microscopic “images” or “snapshots” of a phase, created using a special topography procedure, are fed into a machine-learning algorithm that is trained to decide whether these images come from a topological or a conventional phase of matter—exactly as modern computer vision algorithms are designed to tell cats from dogs in a picture. https://physics.aps.org/articles/v10/56

The experimental realization of ultrathin graphene – which earned two scientists from the University of Manchester, U.K., the Nobel Prize in physics in 2010 – has ushered in a new age in materials research. What started with graphene has evolved to include numerous related single-atom-thick materials, which have unusual properties due to their ultra-thinness. Among them are transition metal dichalcogenides (TMDs), materials that offer several key features not available in graphene and are emerging as next-generation semiconductors. TMDs could realize topological superconductivity and thus provide a platform for quantum computing – the ultimate goal of a Cornell research group led by Eun-Ah Kim, associate professor of physics. Read More: http://news.cornell.edu/stories/2017/04/group-works-toward-devising-next...