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An independent review of the Gordon and Betty Moore Foundation's Emergent Phenomena in Quantum Systems (EPiQS) Initiative was recently conducted to assess the quality and impact of this $90M program (see report here). The EPiQS five-year program was aimed to stimulate breakthroughs that fundamentally change our understanding of the organizing principles of complex matter in solid materials. In its first phase, the initiative supported experimental investigators, materials synthesis investigators and fellows, and theory centers, along with funding for equipment grants and community- building activities. The Moore Foundation has just announced it is going to continue this program into a second phase, adding $95M for the next six years to support discovery-driven research in this rapidly growing field.
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Semimetalic MoTe2 is an exciting material exhibiting both type-II Weyl nodes and superconductivity. Because broken inversion symmetry is required for the Weyl semimetal phase, the structural phase transition between inversion symmetric (1T’) and nonsymmetric phases (Td) in this material complicates the interpretation of both the topology and the role of superconductivity. A collaboration between NCNR researchers led by Colin Heikes, together with CNAM grad student I-Lin Liu, has combined pressure-dependent neutron scattering, transport measurements, and first-principles calculations to deconvolve the structural phase transformation from the superconducting transition. Unexpectedly, both structural phases support superconductivity, and the authors show that anisotropic strain can be used to control which structure accommodates this pressure-enhanced superconductivity. This work was chosen as an Editor's Suggestion in Physical Review Materials.
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A collaboration of CNAM members including Ichiro Takeuchi (MSE), Efrain Rodriguez (Chemistry) and Johnpierre Paglione (Physics), together with researchers from NIST and Duke University,have been exploring methods of using artificial intelligence techniques to explore new compounds and search for new, practical superconducting materials by developing machine learning schemes to model the critical temperature (Tc) of over 12,000 known superconductors. Led by postdoctoral research Valentin Stanev, this project involved training a classification model based only on chemical compositions to categorize the known superconductors using a random forest model, and developing regression models to predict the values of Tc for compounds from the database of known materials. Including calculated first principles data from the AFLOW Online Repositories, classification and regression models were combined into a single-integrated pipeline to search the entire Inorganic Crystallographic Structure Database and predict more than 30 new candidate superconductors. This work is now published in NPJ Computational Materials.
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Four University of Maryland undergraduates have been awarded scholarships by the Barry Goldwater Scholarship and Excellence in Education Foundation, which encourages students to pursue advanced study and careers in the sciences, engineering and mathematics. Congratulations to Paul Neves—a junior physics major working on magnetic materials using neutron scattering and other experimental techniques in collaboration with the NIST Center for Neutron Research —who designed and built a new device to improve neutron scattering measurements by simultaneously measuring the bulk magnetic properties of a material under high pressure and at low temperature. Read more here.
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A collaboration between CNAM, CMTC and Ames National Lab researchers led by Prof. J. Paglione has reported experimental evidence of exotic high-spin superconductivity that arises from the unusual electronic structure of the topological semimetal YPtBi. While predicted to occur in other non-material systems, higher than spin-1/2 pairing has remained elusive until now. The team’s research, published in the April 6 issue of Science Advances, reveals effects that are profoundly different from anything that has been seen before with superconductivity.