Multiferroics are materials with coexisting electric and magnetic orders that are of central importance for fundamental research and applications in sensor and information technologies. However, intrinsic multiferroics that operate at room temperature remain rare due to an apparent incompatibility between magnetism and ferroelectricity. Here we predict that a pure ferroelectric hosts multiferroic-like quasiparticles that simultaneously carry \emph{static} magnetic and electric dipoles. The electric dipole moment emerges from the parity-odd anharmonicity of the ferroelectric dynamics, while the magnetic moment has both paramagnetic and diamagnetic origins generated by circularly polarized transverse fluctuations of the ferroelectric polarization. In contrast to the established ``dynamical multiferroicity" of conventional phonons, which involve only \emph{oscillating} electric dipoles, these ``multiferron" modes produce a colossal electric-field-tunable second-harmonic optical response as well as a magnetoelectric cross coupling. Multiferrons open a new route toward nonlinear THz optical applications and offer multiferroic functionalities in simple ferroelectrics.

Quantum magnonics studies the quantum properties of magnons, the quanta of spin waves, and their application in quantum information processing. Progress in this field depends on identifying magnetic materials with characteristics tailored to the diverse requirements of magnonics and quantum magnonics. For single-magnon excitation, its control, hybrid coupling, and entanglement, the most critical property is the ability to support long magnon lifetimes. This perspective reviews established and emerging magnetic materials, including ferromagnetic metals, Heusler compounds, antiferromagnets, altermagnets, organic and 2D van der Waals magnets, hexaferrites, and in particular yttrium iron garnet (YIG), highlighting their key characteristics. YIG remains the benchmark, with bulk crystals supporting sub-microsecond Kittel-mode lifetimes and ultra-pure spheres achieving $\sim18\,\mu$s for dipolar-exchange magnons at millikelvin temperatures. However, thin YIG films on gadolinium gallium garnet (GGG) substrates suffer from severe lifetime reduction due to substrate-induced losses. In contrast, YIG films on a new lattice matched, diamagnetic alternative, yttrium scandium gallium/aluminum garnet (YSGAG), overcomes these limitations and preserves low magnetic damping down to millikelvin temperatures. These advances provide a practical pathway toward ultralong-living magnons in thin films, enabling scalable quantum magnonics with coherent transport, strong magnon-photon, magnon-qubit coupling, and integrated quantum networks.

Graph Neural Networks (GNNs) are the dominant architecture for molecular machine learning, particularly for molecular property prediction and machine learning interatomic potentials (MLIPs). GNNs perform message passing on predefined graphs often induced by a fixed radius cutoff or k-nearest neighbor scheme. While this design aligns with the locality present in many molecular tasks, a hard-coded graph can limit expressivity due to the fixed receptive field and slows down inference with sparse graph operations. In this work, we investigate whether pure, unmodified Transformers trained directly on Cartesian coordinates$\unicode{x2013}$without predefined graphs or physical priors$\unicode{x2013}$can approximate molecular energies and forces. As a starting point for our analysis, we demonstrate how to train a Transformer to competitive energy and force mean absolute errors under a matched training compute budget, relative to a state-of-the-art equivariant GNN on the OMol25 dataset. We discover that the Transformer learns physically consistent patterns$\unicode{x2013}$such as attention weights that decay inversely with interatomic distance$\unicode{x2013}$and flexibly adapts them across different molecular environments due to the absence of hard-coded biases. The use of a standard Transformer also unlocks predictable improvements with respect to scaling training resources, consistent with empirical scaling laws observed in other domains. Our results demonstrate that many favorable properties of GNNs can emerge adaptively in Transformers, challenging the necessity of hard-coded graph inductive biases and pointing toward standardized, scalable architectures for molecular modeling.

In solid state materials, gradients of the electro-chemical potential, the temperature, or the spin-chemical potential drive the flow of charge, heat, and spin angular momentum, resulting in a net transport of energy. Beyond the primary transport processes - such as the flow of charge, heat, and spin angular momentum driven by gradients in their respective potentials - a wide range of coupled or cross-linked transport responses can occur, giving rise to a rich variety of transport phenomena. These transport phenomena are commonly categorized under (anomalous) thermoelectric, thermomagnetic, and galvanomagnetic effects, along with their spin-dependent counterparts. However, establishing a systematic classification and comparison among them remains a complex and nontrivial task. This paper attempts a didactic overview of the different transport phenomena, by categorizing and briefly discussing each of them based on charge, heat, and spin transport in conducting solids. The phenomena are structured in three categories: collinear, transverse, and so-called `planar' transport effects. The resulting overview attempts to categorize all effects in a consistent manner.

Scientific progress increasingly depends on synthesizing knowledge across vast literature, yet most experimental data remains trapped in semi-structured formats that resist systematic extraction and analysis. Here, we present MatSKRAFT, a computational framework that automatically extracts and integrates materials science knowledge from tabular data at unprecedented scale. Our approach transforms tables into graph-based representations processed by constraint-driven GNNs that encode scientific principles directly into model architecture. MatSKRAFT significantly outperforms state-of-the-art large language models, achieving F1 scores of 88.68 for property extraction and 71.35 for composition extraction, while processing data $19$-$496\times$ faster than them (compared to the slowest and the fastest models, respectively) with modest hardware requirements. Applied to nearly 69,000 tables from more than 47,000 research publications, we construct a comprehensive database containing over 535,000 entries, including 104,000 compositions that expand coverage beyond major existing databases, pending manual validation. This systematic approach reveals previously overlooked materials with distinct property combinations and enables data-driven discovery of composition-property relationships forming the cornerstone of materials and scientific discovery.

In this Letter, we report the observation of disorder-driven anisotropic Kondo screening and spontaneous Hall effect in bulk WTe${_2}$, a nonmagnetic type-II Weyl semimetal. We show that Kondo scattering emerges more prominently in disordered samples and produces magnetoresistance that is strongly anisotropic with respect to both current and magnetic field orientation, reflecting the underlying type-II Weyl dispersion. Strikingly, we find a spontaneous Hall effect in zero magnetic field, whose magnitude is enhanced with disorder, together with a large second-harmonic Hall signal exhibiting quadratic current scaling. Our analysis indicates that disorder-driven Kondo interactions pin the Fermi level near the Weyl nodes. This enhances the Berry curvature-driven nonequilibrium transport, accounting for both the second-order and spontaneous Hall responses. These findings establish disordered WTe${_2}$ as a platform hosting Weyl-Kondo fermions and highlight disorder as an effective control knob for inducing correlated topological phases in weakly correlated Weyl semimetals.

The lack of a unified theoretical framework for characterizing band inversions across different crystal symmetries hinders the rapid development of topological photonic band engineering. To address this issue, we have constructed a framework constrained by symmetry $k \cdot p$ that universally models bands near high-symmetry points for symmetric photonic crystals $C_6$, $C_4$, $C_3$, and $C_2$. This framework enables a coefficient-free quantitative diagnosis of band topology. We have demonstrated the power of this framework by systematically engineering band inversions. In $C_6$ crystals, we induce a reopening of the linear gap at $\Gamma$. In $C_4$ systems, mirror symmetry enforces a characteristic quadratic coupling leading to distinct spectral features. Our analysis further reveals that a lone $E$ doublet prevents inversion at the $\Gamma$ point in $C_3$ symmetry, while $C_2$ symmetry facilitates a unique inversion of $Y$ pointsints with anisotropic gap. This symmetry-first, fit-free approach establishes a direct link between experimental band maps and the extraction of fundamental topological parameters. It offers a universal tool for inversion and coupling-order identification.

Dislocation jogs have strong effects on dislocation motion that governs the strain-hardening behavior of crystalline solids, but how to properly account for their effect in mesoscale models remains poorly understood. We develop a mobility model for jogged edge dislocations in FCC nickel, based on systematic molecular dynamics (MD) simulations across a range of jog configurations, stresses, and temperatures. At low stresses, jogged edge dislocations exhibit non-linear, thermally activated dragging and a higher Peierls barrier compared to straight dislocations. Surprisingly, stress-velocity curves for a given jog configuration across varying temperatures intersect at an invariant point ($\tau_{\rm c}$, $v_{\rm c}$), where $\tau_{\rm c}$ delineates thermally-activated and phonon-drag regimes and is close to the Peierls stress ($\tau_{\rm p}$). Motivated by this observation, we propose a simple three-section expression for jogged dislocation mobility, featuring minimal and physically interpretable parameters. This mobility law offers a realistic description of jog effects for dislocation dynamics (DD) simulations, improving their physical fidelity for crystal plasticity predictions.

Odd-parity spin-splitting plays a central role in spintronics and unconventional superconductivity, yet its microscopic realization in collinear magnetic systems remains elusive. We propose a general symmetry-based strategy for realizing odd-parity altermagnetism by stacking two noncentrosymmetric monolayers in an interlayer antiferromagnetic configuration and applying an in-plane layer-flip operation. In this setting, odd-parity spin-splitting originates from nonrelativistic orbital orders rather than spin-orbit coupling, and is protected by an effective time-reversal symmetry despite the explicit time-reversal symmetry being broken. By exploiting lattice symmetries, our framework enables the realization of both $p$- and $f$-wave altermagnets. The resulting models generically host quantum spin Hall insulator phases, featuring topologically protected helical edge states and quantized spin Hall conductance. Our work expands the landscape of altermagnetic phases and opens a pathway toward spintronics and unconventional superconductivity in altermagnetic systems.

We present a benchmark designed to evaluate the predictive capabilities of universal machine learning interatomic potentials across systems of varying dimensionality. Specifically, our benchmark tests zero- (molecules, atomic clusters, etc.), one- (nanowires, nanoribbons, nanotubes, etc.), two- (atomic layers and slabs) and three-dimensional (bulk materials) compounds. The benchmark reveals that while all tested models demonstrate excellent performance for three-dimensional systems, accuracy degrades progressively for lower-dimensional structures. The best performing models for geometry optimization are orbital version 2, equiformerV2, and the equivariant Smooth Energy Network, with the equivariant Smooth Energy Network also providing the most accurate energies. Our results indicate that the best models yield, on average, errors in the atomic positions in the range of 0.01-0.02 angstrom and errors in the energy below 10~meV/atom across all dimensionalities. These results demonstrate that state-of-the-art universal machine learning interatomic potentials have reached sufficient accuracy to serve as direct replacements for density functional theory calculations, at a small fraction of the computational cost, in simulations spanning the full range from isolated atoms to bulk solids. More significantly, the best performing models already enable efficient simulations of complex systems containing subsystems of mixed dimensionality, opening new possibilities for modeling realistic materials and interfaces.

Accurately modeling interfacial thermal transport in van der Waals heterostructures is challenging due to the limited availability of interlayer interaction potentials. We develop a pairwise interlayer potential for graphene/germanene van der Waals heterostructure using the binding energy obtained from ab-initio density functional theory calculations and use it to calculate the interfacial thermal conductivity. Our calculations reveal that the interfacial thermal conductivity shows superior tunability with external strain. The phonon density of states calculations show a blueshift in the phonon spectra with an applied compressive strain in the direction of heat flow, increasing the interfacial thermal conductance to $\sim$136% of the unstrained value. In contrast, a tensile strain is found to cause an opposite effect, reducing the conductance to $\sim$70% of the unstrained value. Moreover, due to increased availability of phonons for heat transfer, both temperature and interaction strength are found to correlate positively with the interfacial thermal conductance for both directions of heat flow.

Conventional machine learning approaches accelerate inorganic materials design via accurate property prediction and targeted material generation, yet they operate as single-shot models limited by the latent knowledge baked into their training data. A central challenge lies in creating an intelligent system capable of autonomously executing the full inorganic materials discovery cycle, from ideation and planning to experimentation and iterative refinement. We introduce SparksMatter, a multi-agent AI model for automated inorganic materials design that addresses user queries by generating ideas, designing and executing experimental workflows, continuously evaluating and refining results, and ultimately proposing candidate materials that meet the target objectives. SparksMatter also critiques and improves its own responses, identifies research gaps and limitations, and suggests rigorous follow-up validation steps, including DFT calculations and experimental synthesis and characterization, embedded in a well-structured final report. The model's performance is evaluated across case studies in thermoelectrics, semiconductors, and perovskite oxides materials design. The results demonstrate the capacity of SparksMatter to generate novel stable inorganic structures that target the user's needs. Benchmarking against frontier models reveals that SparksMatter consistently achieves higher scores in relevance, novelty, and scientific rigor, with a significant improvement in novelty across multiple real-world design tasks as assessed by a blinded evaluator. These results demonstrate SparksMatter's unique capacity to generate chemically valid, physically meaningful, and creative inorganic materials hypotheses beyond existing materials knowledge.