This paper presents a fully ab initio many-body photoemission framework that includes coherent three-body electron-photon-phonon scattering to predict the transverse momentum distributions and the mean transverse energies (MTEs) of bulk photoelectrons from single-crystal photocathodes. The need to develop such a theory stems from the lack of studies that provide complete understanding of the underlying fundamental processes governing the transverse momentum distribution of photoelectrons emitted from single crystals.

The evolution of a Landau Fermi liquid into a non-magnetic Mott insulator with increasing electronic interactions is one of the most puzzling quantum phase transitions in physics1–6. The vicinity of the transition is believed to host exotic states of matter such as quantum spin liquids4–7, exciton condensates8 and unconventional superconductivity1. Semiconductor moiré materials realize a highly controllable Hubbard model simulator on a triangular lattice9–22, providing a unique opportunity to drive a metal–insulator transition (MIT) via continuous tuning of the electronic interactions.

The discovery of substrate materials has been dominated by trial and error, opening the opportunity for a systematic search. We generate bonding networks for materials from the Materials Project and systematically break up to three bonds in the networks for three-dimensional crystals. Successful cleavage reduces the bonding network to two periodic dimensions. We identify 4693 symmetrically unique cleavage surfaces across 2133 bulk crystals, 4626 of which have a maximum Miller index of one.

Designing flat sheets that can be made to deform into three-dimensional shapes is an area of intense research with applications in micromachines, soft robotics, and medical implants. Thus far, such sheets were designed to adopt a single target shape. Here, we show that through anisotropic deformation applied inhomogeneously throughout a sheet, it is possible to design a single sheet that can deform into multiple surface geometries upon different actuations. The key to our approach is development of an analytical method for solving this multivalued inverse problem.

Motivated by recent experiments, we calculate particle emission from a Bose-Einstein condensate trapped in a single deep well of a one-dimensional lattice when the interaction strength is modulated. In addition to pair emission, which has been widely studied, we observe single-particle emission. Within linear response, we are able to write closed-form expressions for the single-particle emission rates and reduce the pair emission rates to one-dimensional integrals.

We introduce a new toy model for the study of glasses: the hard-matrix model. This may be viewed as a single particle moving on SO(N), where there is a potential proportional to the one-norm of the matrix. The ground states of the model are 'crystals' where all matrix elements have the same magnitude. These are the Hadamard matrices when N is divisible by four. Just as finding the latter has challenged mathematicians, our model fails to find them upon cooling and instead shows all the behaviors that characterize physical glasses.

Efficient generation of spin-orbit torques is central for the exciting field of spin-orbitronics. Platinum, the archetypal spin Hall material, has the potential to be an outstanding provider for spin-orbit torques due to its giant spin Hall conductivity, low resistivity, high stabilities, and the ability to be compatible with CMOS circuits. However, pure clean-limit Pt with low resistivity still provides a low damping-like spin-orbit torque efficiency, which limits its practical applications.

Time-resolved crystallography of biomolecules in action has advanced rapidly as methods for serial crystallography have improved, but the large number of crystals and the complex experimental infrastructure that are required remain serious obstacles to its widespread application. Here, millisecond mix-and-quench crystallography (MMQX) has been developed, which yields millisecond time-resolved data using far fewer crystals and routine remote synchrotron data collection.

Recent experiments have shown that a deep neural network can be trained to predict the action of t steps of Conway's Game of Life automaton given millions of examples of this action on random initial states. However, training was never completely successful for t>1, and even when successful, a reconstruction of the elementary rule (t=1) from t>1 data is not within the scope of what the neural network can deliver. We describe an alternative network-like method, based on constraint projections, where this is possible.

Multiple human tissue engineered cartilage constructs are showing promise in advanced clinical trials but identifying important measures of manufacturing reproducibility remains a challenge. FDA guidance suggests measuring multiple mechanical properties prior to implantation, because these properties could affect the long term success of the implant. Additionally, these engineered cartilage mechanics could be sensitive to the autologous chondrocyte source, an inherently irregular manufacturing starting material.