Charge density waves in kagome metals are typically identified through long-range structural order, yet their fluctuating counterparts may play an equally important role in shaping electronic phases. Using ultrafast coherent phonon spectroscopy, the authors reveal that in hole-doped CsV₃Sb₅, strong charge density wave fluctuations persist far beyond the disappearance of static order, with picosecond correlation times. These fluctuations peak near a doping-tuned quantum phase transition that coincides with a minimum in the superconducting double dome. Their results establish fluctuating charge order as a robust and ubiquitous feature of kagome metals and highlight its potential influence on superconductivity and other emergent quantum phenomena.

Refractory metal compounds are difficult to synthesize due to the extreme thermodynamic windows required, and therefore deconvoluting intrinsic properties from extrinsic effects can be challenging. This work dives into the synthesis and electronic properties of thin films of the refractory metal oxide NbO utilizing ultrahigh growth temperatures. The highlight is the ability to access a “thermally activated epitaxy” growth regime at very high temperatures (T > 1000 °C), which enables reproducible synthesis across a wide range of oxygen partial pressures. Using samples grown in this regime, the authors propose the prototypical electrical properties of NbO, for which a consensus has not yet been made in the literature.

The VCoNi alloy is a face-centered cubic medium-entropy alloy with exceptional strength and serves as a model system to study short-range order and phase stability in compositionally complex alloys. Density functional theory, however, underestimates the stability of the random solid solution by several hundred Kelvin. We resolve this discrepancy through accurate Gibbs energy calculations for both the disordered solid solution and a representative L1${}_2$ ordered phase. Vibrational and electronic excitations account for nearly half of the entropy difference between the phases, reduce the ordering energy by about one-third, and significantly enhance the stability of the solid solution, in agreement with experimental thermodynamic data.

The shock loading responses of Sn have attracted significant interest. Although atomistic simulations have been useful for uncovering nano-scale mechanisms behind experimental observations, exiting potentials of Sn lack sufficient accuracy especially for predicting its complex high-pressure phase transitions. To overcome this challenge, the authors construct DP-SCAN-S, an machine learning potential trained on comprehensive DFT data spanning an extensive thermodynamic range from 0–100 GPa pressure and 0–5000 K temperature. It accurately reproduces DFT-derived basic properties, experimental melting curves, solid-solid phase boundaries, and shock Hugoniot results. This demonstrates the model’s potential to bridge ab initio precision with large-scale dynamic simulations.

Magnetism and magnon excitation in the two-dimensional van der Waals CrI${}_3$ is driven by not only the isotropic spin-exchange interactions, but also the anisotropic spin-exchange interactions, where the nonmagnetic ligand Iodine atoms play important roles. This allows indirect modulation of interactions between Chromium atoms by tuning the electronic states of Iodine atoms via stacking order control. Here, combining first-principles calculation and linear spin-wave theory, the authors theoretically demonstrate that multiple stacking orders between monolayer CrI${}_3$ and Arsenic host different modulation of spin-exchange interactions and topological magnon phases, identified by the existence of chiral edge states. The modulation of spin-exchange interactions is mainly driven by the interfacial charge transfer from the Arsenic atoms to the Iodine atoms, which indirectly changes the electronic states of Chromium 𝑑 orbitals from symmetry point of view.

Magnetic Weyl semimetals such as Co2MnGa (CMG) are promising candidates for next-generation spintronic materials due to their exotic topological properties. Using ferromagnetic resonance spectroscopy, the authors investigate CMG thin films, revealing that the thermal evolution of the exchange stiffness is dominated by electron-magnon interactions. Furthermore, they demonstrate ultralow damping at room temperature in all the films, with the thickest film showing a temperature-independent damping behavior down to 10 K. These results highlight CMG’s potential for efficient room‑temperature and cryogenic magnonic circuits and provide critical parameters for micromagnetic modeling to support device engineering.

NV centers in diamond are promising quantum sensors, but when placed only nanometers below the host surface they are disturbed by every microscopic detail of the surface and they quickly lose their coherence properties. First principles atomistic modeling combined with spin dynamics simulations show that the culprit is not just the type of spins living at the surface, but how they move: surface noise is dynamical. Termination chemistry and facet orientation do matter, but surface-electron relaxation and hopping dominate the coherence of shallow NVs.

Indium phosphide (InP) is a promising photocatalyst, yet its performance is often limited by fast carrier recombination and inefficient charge transport. Deng \emph{et al}. use scanning ultrafast electron microscopy (SUEM) to directly visualize photoexcited carrier dynamics on femtosecond-nanometer scales at the InP surface decorated with silver nanoparticles. SUEM reveals that Ag nanoparticles remarkably enhance light absorption via localized surface plasmon resonance, leading to stronger charge separation, suppressed recombination, and a pronounced early-time boost in lateral carrier transport. In contrast, a continuous Ag film improves separation through a Schottky junction but shows limited diffusion, underscoring the unique role of plasmonic nanoparticles.

La${}_{1-x}$Sr${}_x$CoO${}_{3-\delta }$ (LSCO) is known to undergo an electrochemically driven perovskite-to-brownmillerite transition with wide modulation of electronic, magnetic, thermal, and optical properties. However, the extended reversibility of this transition remains unproven, with repeated cycling leading to performance degradation via acid-etching, particularly in humid environments. Here, combining density functional theory with an out-of-the-box universal machine learning interatomic potential, the authors demonstrate that hydrogen insertion in LSCO is thermodynamically favorable over a wide range of conditions, but ultimately destabilizes the host structure towards decomposition. Metastable protonated phases are expected to exhibit significant structural expansion and band gap widening, mirroring the effect of oxygen vacancies and highlighting the need for caution when interpreting experimental results for electrochemically-gated LSCO and related materials.

Semiconductor compounds are often alloyed to obtain target physical properties that are absent in the individual components. Conventional tetrahedral semiconductors generally have lower alloy gaps than the composition average gap of the constituents (“downward bowing”). We designed via DFT multi-component halide perovskite alloys that have significant upward bowing. Such alloys have a rather low mixing enthalpy, suggesting stability towards phase separation. The enabling idea is to mix perovskites with B atoms that have low lying s-orbitals in the valence band, with a compound that has IB atoms (e.g., Cd) with s-orbitals in the conduction band. The ensuing s-s repulsion opens the alloy gap with respect to the constituents’ gap.

Predicting yielding in metallic glasses remains challenging because plasticity emerges without clear structural precursors. Here, a physically grounded Bayesian framework is introduced to predict the stress-strain response up to the yield point using plastic strain accumulation already in the nominally elastic regime. Across Cu-Zr(-Al) metallic glasses with varying annealing, two limiting growth laws for plastic strain, power-law and exponential, are identified and linked to distinct microscopic plastic activity patterns. By inferring these growth parameters from stress-strain data below 5% strain, the approach enables early, interpretable predictions of macroscopic deformation and failure.