QMC researchers present a protocol to synthesize two types of UTe2 crystals -- those exhibiting robust superconductivity, via chemical vapor transport synthesis, and those lacking superconductivity, via molten metal flux synthesis -- in an open source publication in the journal JoVE called "Comparison of Two Different Synthesis Methods of Single Crystals of Superconducting Uranium Ditelluride". The online video tutorial is found here.
Scores of University of Maryland programs earned accolades in U.S. News & World Report’s 2023 Best Graduate Schools lists released today, with 56 top-25 rankings in overall and specialty categories.See rankings at US News and World Reports.
UMD invites applications for multiple faculty positions at all levels in quantum science and information.There are two portals: 1) College of Computer, Math & Natural Sciences (Physics, Computer Science, Mathematics, Chemistry and Biochemistry -- CMNS link here) and 2) A. James Clark School of Engineering (Electrical and Computer Engineering, Materials Science Engineering, Mechanical Engineering -- ENGR link here). The Campus has established four internationally recognized multi-disciplinary research institutes and centers to promote quantum research, the Joint Quantum Institute (JQI), the Joint Center for Quantum Information and Computer Science (QUICS), the Quantum Technology Center (QTC). and the Quantum Materials Center (QMC). In addition, faculty actively engage with other institutes and centers in the College, such as the Institute for Systems Research (ISR), the Institute for Advanced Computer Studies (UMIACS), the Maryland Robotics Center (MRC), the Maryland Cyber-security Center (MC2), and the Institute for Research in Electronics and Applied Physics (IREAP). All areas of quantum science and information will be considered, including quantum computation, quantum simulation, quantum information processing, quantum materials, quantum sensing, and quantum networking. Research can be experimental, theoretical or computational in nature.
Artificial intelligence and machine learning are becoming indispensable tools in many areas of physics, including astrophysics, particle physics, and climate science. In the arena of quantum materials, the rise of new experimental and computational techniques has increased the volume and the speed with which data are collected, and artificial intelligence is poised to impact the exploration of new materials such as superconductors, spin liquids, and topological insulators. This review by QMC members Ichiro Takeuchi and Johnpierre Paglione outlines how the use of data-driven approaches is changing the landscape of quantum materials research. See more in the Communications Materials online publication.