Three-dimensional collective charge excitations in electron-doped copper oxide superconductors

A collaboration of CNAM members including Richard L. Greene and Tarapada Sarkar, together with researchers from SLAC, ESRF, CNR-Spin and Binghamton University, has been exploring the Three-dimensional collective charge excitations in electron-doped copper oxide superconductors.

High-temperature copper oxide superconductors consist of stacked CuO2 planes, with electronic band structures and magnetic excitations that are primarily two-dimensional, but with superconducting coherence that is three-dimensional. This dichotomy highlights the importance of out-of-plane charge dynamics, which has been found to be incoherent in the normal state, within a limited range of momenta accessible by optics. In this research, we used resonant inelastic X-ray scattering to RIXS explore the charge dynamics across all three dimensions of the Brillouin zone. Polarization analysis of recently discovered collective excitations (modes) in electron-doped copper oxides reveals their charge origin, that is, without mixing with magnetic components. The excitations disperse along both the in-plane and out-of-plane directions, revealing its three-dimensional nature. The periodicity of the out-of-plane dispersion corresponds to the distance between neighboring CuO2 planes rather than to the crystallographic c-axis lattice constant, suggesting that the interplane Coulomb interaction is responsible for the coherent out-of-plane charge dynamics. The observed properties are hallmarks of the long-sought ‘acoustic plasmon’, which is a branch of distinct charge collective modes predicted for layered systems and argued to play a substantial part in mediating high-temperature superconductivity.

This work is now published October 31, 2018 in Nature International Journal of Science.

Continued Funding for Quantum Materials Science

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.  

Unprecedented Control of Type-II Weyl Semimetal MoTe2

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.

Machine Learning New Superconductors

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.