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Carbon is one of the most important elements for both industrial applications and fundamental research, including life, physics, chemistry, materials, and even planetary science. Although theoretical predictions on the transition from diamond to the BC8 (Ia3[over ¯]) carbon were made more than thirty years ago, after tremendous experimental efforts, direct evidence for the existence of BC8 carbon is still lacking. In this study, a machine learning potential was developed for high-pressure carbon fitted from first-principles calculations, which exhibited great capabilities in modeling the melting and Hugoniot line. Using the molecular dynamics based on this machine learning potential, we designed a thermodynamic pathway that is achievable for the double shock compression experiment to obtain the elusive BC8 carbon. Diamond was compressed up to 584 GPa after the first shock at 20.5  km/s. Subsequently, in the second shock compression at 24.8 or 25.0  km/s, diamond was compressed to a supercooled liquid and then solidified to BC8 in around 1 ns. Furthermore, the critical nucleus size and nucleation rate of BC8 were calculated, which are crucial for nano-second x-ray diffraction measurements to observe BC8 carbon during shock compressions. The key to obtaining BC8 carbon lies in the formation of liquid at a sufficient supercooling. Our work provides a feasible pathway by which the long-sought BC8 phase of carbon can be reached in experiments.

Quantum simulation of different exotic topological phases of quantum matter on a noisy intermediate-scale quantum (NISQ) processor is attracting growing interest. Here, we develop a one-dimensional 43-qubit superconducting quantum processor, named Chuang-tzu, to simulate and characterize emergent topological states. By engineering diagonal Aubry-André-Harper (AAH) models, we experimentally demonstrate the Hofstadter butterfly energy spectrum. Using Floquet engineering, we verify the existence of the topological zero modes in the commensurate off-diagonal AAH models, which have never been experimentally realized before. Remarkably, the qubit number over 40 in our quantum processor is large enough to capture the substantial topological features of a quantum system from its complex band structure, including Dirac points, the energy gap's closing, the difference between even and odd number of sites, and the distinction between edge and bulk states. Our results establish a versatile hybrid quantum simulation approach to exploring quantum topological systems in the NISQ era.

Superconducting and superionic behaviors have physically intriguing dynamic properties of electrons and ions, respectively, both of which are conceptually important and have great potential for practical applications. Whether these two phenomena can appear in the same system is an interesting and important question. Here, using crystal structure predictions and first-principle calculations combined with machine learning, we identify several stable Li-Al compounds with electride behavior under high pressure, and we find that the electronic density of states of some of the compounds has characteristics of the two-dimensional electron gas. Among them, we estimate that Li_{6}Al at 150 GPa has a superconducting transition temperature of around 29 K and enters a superionic state at a high temperature and wide pressure range. The diffusion in Li_{6}Al is found to be affected by an electride and attributed to the atomic collective motion. Our results indicate that alkali metal alloys can be effective platforms to study the abundant physical properties and their manipulation with pressure and temperature.

Artificial spin ices are engineered arrays of dipolarly coupled nanobar magnets. They enable direct investigations of fascinating collective phenomena from their diverse microstates. However, experimental access to ground states in the geometrically frustrated systems has proven difficult, limiting studies and applications of novel properties and functionalities from the low energy states. Here, we introduce a convenient approach to control the competing diploar interactions between the neighboring nanomagnets, allowing us to tailor the vertex degeneracy of the ground states. We achieve this by tuning the length of selected nanobar magnets in the spin ice lattice. We demonstrate the effectiveness of our method by realizing multiple low energy microstates in a kagome artificial spin ice, particularly the hardly accessible long range ordered ground state-the spin crystal state. Our strategy can be directly applied to other artificial spin systems to achieve exotic phases and explore new emergent collective behaviors.

Although the transport and mixing of proteins and other molecules inside bacteria rely on the diffusion of molecules, many aspects of the molecular diffusion in bacterial cytoplasm remain unclear or controversial, including how the diffusion-temperature relation follows the Stokes-Einstein equation. In this study, we applied single-particle tracking photoactivated localization microscopy to investigate the diffusion of histonelike nucleoid structuring (HNS) proteins and free dyes in bacterial cytoplasm at different temperatures. Although the diffusion of HNS proteins in both live and dead bacteria increased at higher temperatures and appeared to follow the Arrhenius equation, the diffusion of free dyes decreased at higher temperatures, questioning the previously proposed theories based on superthermal fluctuations. To understand the measured diffusion-temperature relations, we developed an alternative model, in which the bacterial cytoplasm is considered as a polymeric network or mesh. In our model, the Stokes-Einstein equation remains valid, while the polymeric network contributes a significant term to the viscosity experienced by the molecules diffusing in bacterial cytoplasm. Our model was successful in predicting the diffusion-temperature relations for both HNS proteins and free dyes in bacteria. In addition, we systematically examined the predicted diffusion-temperature relations with different parameters in the model, and predicted the possible existence of phase transitions.

A coupled lattice Boltzmann-large eddy simulation model is developed for modeling three-dimensional multiphase flows at large density ratios and high Reynolds numbers. In the framework of the lattice Boltzmann method, the model is proposed based on the standard Smagorinsky subgrid-scale approach, and a reconstructed multiple-relaxation-time collision operator is adopted. The conservative Allen-Cahn equation and Navier-Stokes equations are solved through the lattice Boltzmann discretization scheme for the interface tracking and velocity field evolution, respectively. Relevant benchmark cases are carried out to validate the performance of this model in simulating multiphase flows at a large density ratio and a high Reynolds number, including a stationary droplet, the process of spinodal decomposition, the Rayleigh-Taylor instability, the phenomenon of a droplet splashing on a thin liquid film, and the liquid jet breakup process. The maximum values of density ratio and Re number are 1000 and 10 240, respectively. The capability and reliability of the proposed model have been demonstrated by the good agreement between simulation results and the analytical solutions or the previously available results.