The interplay between strong spin-orbit coupling and electron correlations has recently been the subject of intense investigation, due to a number of theoretically predicted phases such as quantum spin liquids, unconventional superconductivity, complex magnetic orders, and correlated topological phases of matter. In particular, iridates have been proposed as a promising family of materials which could host a number of these phases.

The Sachdev-Ye-Kitaev (SYK) model incorporates rich physics, ranging from exotic non-Fermi liquid states without quasiparticle excitations, to holographic duality and quantum chaos. However, its experimental realization remains a daunting challenge due to various unnatural ingredients of the SYK Hamiltonian such as its strong randomness and fully nonlocal fermion interaction. At present, constructing such a nonlocal Hamiltonian and exploring its dynamics is best through digital quantum simulation, where state-of-the-art techniques can already handle a moderate number of qubits.

Spin-orbit torques (SOT) in thin film heterostructures originate from strong spin-orbit interactions (SOI) that, in the bulk, generate a spin current due either to extrinsic spin-dependent, skew, or/and side-jump scattering or to intrinsic Berry curvature in the conduction bands. While most SOT studies have focused on materials with heavy metal components, the oxide perovskite SrRuO3 has been predicted to have a pronounced Berry curvature.

Increasing dampinglike spin-orbit torque (SOT) is of fundamental importance for enabling new research into spintronics phenomena and also technologically urgent for advancing low-power spin-torque memory, logic, and oscillator devices. Here, we demonstrate that enhancing interfacial scattering by inserting ultrathin layers within spin Hall metals with intrinsic or side-jump mechanisms can significantly enhance the spin Hall ratio.

High magnetic fields suppress cuprate superconductivity to reveal an unusual density wave (DW) state coexisting with unexplained quantum oscillations. Although routinely labeled a charge density wave (CDW), this DWstate could actually be an electron-pair density wave (PDW). To search for evidence of a field-induced PDW, we visualized modulations in the density of electronic states N(r) within the halo surrounding Bi2Sr2CaCu2O8 vortex cores.

Embryonic development is orchestrated by robust and complex regulatory mechanisms acting at different scales of organization. In vivo studies are particularly challenging for mammals after implantation, owing to the small size and inaccessibility of the embryo. The generation of stem cell models of the embryo represents a powerful system with which to dissect this complexity. Control of geometry, modulation of the physical environment, and priming with chemical signals reveal the intrinsic capacity of embryonic stem cells to make patterns.

We conduct a comprehensive study of three different magnetic semiconductors, CrI3, CrBr3, and CrCl3, by incorporating both few-layer and bilayer samples in van der Waals tunnel junctions. We find that the interlayer magnetic ordering, exchange gap, magnetic anisotropy, and magnon excitations evolve systematically with changing halogen atom. By fitting to a spin wave theory that accounts for nearest-neighbor exchange interactions, we are able to further determine a simple spin Hamiltonian describing all three systems.

Heaven is in the Cloud in this new tome. Paul McEuen watches in wonder. © 2019, Nature.

This article describes a highly parallel event driven interface between a Pixel Array Detector (PAD) and its processing electronics. The method used was originally developed for the Field Programmable Gate Array (FPGA) X-ray Pixel Array detector to allow for real-time processing of X-ray image data on its processing FPGA. This interface potentially allows for entirely asynchronous data transfer off the detector at rates exceeding 110Gbps and operates without the need for a constantly running clock.

For centuries, the scientific discovery process has been based on systematic human observation and analysis of natural phenomena1. Today, however, automated instrumentation and large-scale data acquisition are generating datasets of such large volume and complexity as to defy conventional scientific methodology. Radically different scientific approaches are needed, and machine learning (ML) shows great promise for research fields such as materials science2–5.