High-throughput synthesis and characterization of novel ceramic materials with improved thermomechanical properties and phase stability are needed to accelerate the discovery of next-generation thermal barrier materials. A combinatorial thin film material library of (GdDyHoEr)2Zr2O7 was created via combinatorial magnetron reactive sputtering with rare-earth/zirconium alloy targets. Structural, chemical, and thermal property characterization mapping across the four component composition space was performed and correlated with thermal transport measurements. Steady state thermoreflectance mapping identifies a pronounced minimum in thermal conductivity within the Dy/Gd-rich quadrant. This minimum does not coincide with either the equiatomic composition or the region predicted to exhibit maximum cation size disorder. Instead, it corresponds to the largest experimentally observed lattice parameter, despite deviating from Vegard-like chemical averaging, and is independent of grain size and whole-pattern microstrain. These observations suggest that the way the fluorite lattice accommodates compositional complexity, rather than cation size disorder alone, provides a more informative descriptor of thermal transport. Overall, this work establishes a high-throughput workflow for combinatorial thin-film synthesis and multimodal characterization, enabling the rapid identification of previously inaccessible structure-property relationships in compositionally complex ceramics.

Rapid advances in agentic artificial intelligence enable scientific systems to interpret open-ended objectives, combine heterogeneous information, invoke specialized tools, and revise experimental strategies as evidence accumulates. However, physical experimentation also contains many tasks for which agentic reasoning provides little advantage and can reduce reliability. Quantitative analysis, optimization, spatial targeting, validation, and instrument execution are often better posed as deterministic or algorithmic operations with explicit objectives and verifiable outputs. Here, we introduce a hierarchical architecture for autonomous experimentation that separates these roles. Agentic components interpret scientific intent, construct task-dependent experimental representations, evaluate accumulated evidence, and select high-level actions, whereas deterministic algorithms perform numerical analysis, coordinate selection, validation, and physical execution. We implement this architecture in piezoresponse force microscopy. Starting from a broad scientific question concerning the relation between local domain structure and polarization switching, the system constructs spatial descriptors from multichannel imaging, selects and analyzes local hysteresis measurements, adapts the spectroscopy waveform, and terminates the experiment when additional measurements cease to provide new evidence. The autonomous trajectory also identifies a confounding relationship between polarization state and domain-wall proximity and recognizes that the requested contrast is not independently represented within the available field of view. These results demonstrate a route toward scientific autonomy in which agents determine what evidence is required while algorithms determine how that evidence is acquired reproducibly and within validated physical constraints.

The soft-mode of electro-optic metal-oxide perovskites plays a critical role in determining their Pockels responses. This is the case of potassium tantalate niobate (KTN), which exhibits an intrinsic Pockels response 2.5 times larger than that of state-of-the-art barium titanate (BTO), highlighting its potential for high-performance electro-optic applications. By combining ab initio calculations at the density-functional theory level and far-IR measurements, we reveal that the harmonic approximation combined with semi-local exchange-correlation functionals fails to accurately capture the soft transverse optical Slater mode of both potassium niobate and KTN, which dominates the technologically relevant r51 Pockels coefficient. Replacing the calculated mode frequency with its measured value provides an experimentally constrained approach that substantially improves the predicted Pockels response. For that purpose, the previously unreported transverse-optical Slater-mode frequency of KTN is extracted from far-IR reflectivity measurements. The results emphasize the potential of KTN-based electro-optic modulators as an alternative to standard lithium niobate and BTO technologies, with a potentially lower energy consumption and device footprint.

Topological spin textures in local moment systems hold great promise for technological applications due to their large magnetic moments, strong spin-orbit coupling, and high tunability. Finding new spin textures that are stable near room temperature is paramount to maximizing their potential for applications. Here, we provide a strategy for realizing topological spin textures at high temperatures by identifying rare earth magnets ordering at or near room temperature. We demonstrate the feasibility of this strategy in one of these magnets, hexagonal Gd₅Pb₃, which orders at 285 K. The indication for topological spin textures comes from topological Hall effect, which, in Gd₅Pb₃, occurs between 100 and 200 K, an order of magnitude higher temperature than in other reported rare-earth-based systems. Our results present an opportunity to explore the role of spin-orbit coupling, anisotropic exchange, geometric frustration, and magnetic interactions in stabilizing topological spin textures, and provide a pathway toward realizing them near room temperature in rare-earth-based magnets.

Hydrodynamic corrections to diffusive transport can arise when momentum-conserving collisions between quasiparticles become prominent, and they have been documented for both electrons and phonons. An emerging frontier topic is coupled electron-phonon hydrodynamics. Here, through electrical and thermal transport measurements on TaAs₂ crystals with different impurity levels, we document the emergence of an electron-phonon bifluid in the temperature window of 5 to 15 K. Within this range, the lattice thermal conductivity exhibits a faster-than-T3 temperature dependence, as a consequence of non-monotonic and purity-dependent phonon mean free paths, a signature of phonon Poiseuille flow. However, strong electron-phonon coupling impedes the emergence of a ballistic regime. This is corroborated by the observation of quantum oscillations in the lattice thermal conductivity. Prominent phonon-mediated momentum exchange between electrons amplifies the violation of the Wiedemann-Franz law and yields a two-order-of-magnitude discrepancy between quantum and transport lifetimes, a signature of electron hydrodynamics in semimetals. Our results imply that in semimetals with optimized electron-phonon coupling, thanks to matching between the cryogenic phonon wavelength and the Fermi wavelength, momentum and energy flow between the electron and phonon reservoirs as frequently as within each reservoir.

SALTED provides an open-source Python package for machine learning the quantum-mechanical electron density in molecular and condensed-phase systems based on input atomic coordinates and species. The program adopts a linear atom-centered decomposition of the electron density, which makes it highly transferable across diverse atomistic configurations sharing similar chemical environments. Because of this representation choice, SALTED is naturally interfaced with state-of-the-art electronic-structure programs based on atomic orbitals, namely CP2K, FHI-aims, and PySCF, from which reference electron-density data can be generated and used to train a model. The learning algorithm is based on a symmetry-adapted extension of Gaussian process regression, making SALTED especially efficient in small-data regimes. Thanks to the implementation of vector-field kernel functions, SALTED can also learn the first-order response of the electron density to applied electric fields. The application of SALTED within computational workflows has already shown its utility in a wide variety of contexts, including the calculation of polarization vectors and polarizability tensors, the accurate evaluation of Coulomb forces in QM/MM molecular-dynamics simulations, and electronic-structure studies of large-scale 2D materials.