Papers

Rutile germanium dioxide (r-GeO2) has recently emerged as a promising ultrawide-bandgap (UWBG) semiconductor owing to its wide bandgap (~4.4-5.1 eV), ambipolar doping potential, and high theoretical thermal conductivity. However, experimental data on the thermal conductivity of r-GeO2 epitaxial layers have not been reported, primarily due to challenges in phase control and surface roughness. Here, we report a high thermal conductivity of 52.9 +/- 6.6 W m^-1 K^-1 for high-quality (002) r-GeO2 films grown by metal-organic chemical vapor deposition (MOCVD) and characterized using time-domain thermoreflectance (TDTR). The phase control was achieved through a seed-driven stepwise crystallization (SDSC) approach, and the surface roughness was significantly reduced from 76 nm to 16 nm (locally as low as 1 A) via chemical mechanical polishing (CMP). These results highlight the promise of r-GeO2 as a UWBG oxide platform for power electronics applications.

In this work, we analytically derive a semi-classical equation of motion describing the zitterbewegung effects arising in the dynamics of wavepackets in non-Hermitian systems. In Hermitian non-relativistic quantum systems, the zitterbewegung effects can arise due to the spin precession and spin-orbit coupling. Interestingly, the spin dynamics in non-Hermitian systems are qualitatively different because of the effective nonlinear terms induced by the non-Hermitian part of the Hamiltonian. In this work, we show the effects from the non-Hermitian spin dynamics by generalising the description of zitterbewegung effects to non-Hermitian systems. We also uncover novel non-Hermitian correction to the group velocity, which can be expressed in terms of the non-Hermitian quantum metric tensor in the absence of out-of-plane effective field.

The parity anomaly for Dirac fermions in two spatial dimensions has shaped perspectives in quantum field theory and condensed matter physics. In condensed matter it has evolved as a mechanism for half-quantized Hall responses in systems described by massive Dirac fermions. Here we reexamine the issue on a lattice and show that the half-quantized Hall conductivity is absent for massive Dirac fermions when lattice regularization is properly implemented and the translational invariant symmetry is taken into account. We realize that a single massive Dirac cone on a lattice always leads to an integer quantized Hall conductivity and to the half-quantized Hall conductivity only in the unphysical limit of infinite momentum cut-off. The half-quantized Hall conductivity appears with nonzero longitudinal conductance as a signature of a single massless Dirac cone on a lattice. Consequently, the parity anomaly is a property of massless Dirac fermions in a semimetal/metal, not of massive Dirac fermions in an insulator on a lattice.

The remarkable progress of artificial intelligence (AI) has revealed the enormous energy demands of modern digital architectures, raising deep concerns about sustainability. In stark contrast, the human brain operates efficiently on only ~20 watts, and individual cells process gigabit-scale genetic information using energy on the order of trillionths of a watt. Under the same energy budget, a general-purpose digital processor can perform only a few simple operations per second. This striking disparity suggests that biological systems follow algorithms fundamentally distinct from conventional computation. The framework of information thermodynamics-especially Maxwell's demon and the Szilard engine-offers a theoretical clue, setting the lower bound of energy required for information processing. However, digital processors exceed this limit by about six orders of magnitude. Recent single-molecule studies have revealed that biological molecular motors convert Brownian motion into mechanical work, realizing a "demon-like" operational principle. These findings suggest that living systems have already implemented an ultra-efficient information-energy conversion mechanism that transcends digital computation. Here, we experimentally establish a quantitative correspondence between positional information (bits) and mechanical work, demonstrating that molecular machines selectively exploit rare but functional fluctuations arising from Brownian motion to achieve ATP-level energy efficiency. This integration of information, energy, and timescale indicates that life realizes a Maxwell's demon-like mechanism for energy-efficient information processing.

Using particle-resolved molecular-dynamics simulations, we compute the phase diagram for soft repulsive spherocylinders confined on the surface of a sphere. While crystal (K), smectic (Sm), and isotropic (I) phases exhibit a stability region for any aspect ratio of the spherocylinders, a nematic phase emerges only beyond a critical aspect ratio lying between 6.0 and 7.0. As required by the topology of the confining sphere, the ordered phases exhibit a total orientational defect charge of +2. In detail, the crystal and smectic phases exhibit two +1 defects at the poles, whereas the nematic phase features four +1/2 defects which are connected along a great circle. For aspect ratios above the critical value, lowering the packing fraction drives a sequence of transitions: the crystal melts into a smectic phase, which then transforms into a nematic through the splitting of the +1 defects into pairs of +1/2 defects that progressively move apart, thereby increasing their angular separation. Eventually, at very low densities, orientational fluctuations stabilize an isotropic phase. Our simulations data can be experimentally verified in Pickering emulsions and are relevant to understand the morphogenesis in epithelial tissues.

Intensive studies have been made on antiferromagnets as candidate materials for next generation memory bits due to their ultrafast dynamics reaching picosecond time scales. Recent demonstrations of electrical bidirectional switching of antiferromagnetic states have attracted significant attention. However, under the presence of significant Joule heating that destabilizes the magnetic order, the timescales associated with the switching can be limited to nanoseconds or longer. Here, we present the observation of a crossover in the switching behavior of the chiral antiferromagnet Mn3Sn by tuning the magnetic layer thickness. While Joule heating interferes with switching in thicker devices, we find clear signatures of an intrinsic spin-orbit torque mechanism as the thickness is reduced, avoiding the heating effect. The suppression of heating enables switching without significant attenuation of the readout signal using pulses shorter than those required by temperature-assisted mechanisms. The crossover into the spin-orbit torque switching behavior clarifies the potential for achieving ultrafast switching as expected from the picosecond spin dynamics of antiferromagnets. Our results lay the groundwork for designing antiferromagnetic memory devices that can operate at ultrafast timescales.

Many soft jammed materials, such as pastes, gels, concentrated emulsions, and suspensions, possess a threshold stress, known as yield stress, that must be exceeded to cause permanent deformation or flow. In rheology, the term plastic flow is commonly used to describe continuous flow (unbounded increase in strain with time) that a material undergoes above a yield stress threshold. However, in solid mechanics, plasticity refers to irreversible but finite, rate-independent deformation (strain that does not evolve with time). In addition, many soft materials exhibit viscosity bifurcation, a prominent thixotropic signature, which further complicates the definition and interpretation of yield stress. The threshold stress at which viscosity bifurcation occurs is also termed a yield stress, even though deformation below this threshold is not purely elastic, while above this threshold, the material flows homogeneously with a constant shear rate. This paper revisits these critical issues by analyzing the rheological and solid mechanics perspectives on plasticity. The insights presented here are intended to address certain terminological ambiguities for interpreting flow in soft jammed materials.

In this study, we theoretically investigated the magnetic properties of the quasi-two-dimensional organic conductor $\lambda$-(BETS)$_2$GaCl$_4$ using a multi-band Hubbard model and the two-particle self-consistent method. We employed a four-band model in which each BETS molecule is considered as a site and a two-band model that considers each BETS dimer as a site. Our results for the temperature dependence of the Stoner factor reveal a kink around $T_\mathrm{kink} \approx 5 \mathrm{meV}$, which indicates a change in the dominant magnetic fluctuations. A broad structure indicating smeared antiferromagnetic (AFM) fluctuations was observed above $T_\mathrm{kink}$, whereas the spin susceptibility peaked at a wavevector corresponding to spin-density-wave (SDW)-like fluctuations below $T_\mathrm{kink}$. The kink disappears as the intra-dimer transfer integral increased and the AFM fluctuations were enhanced. Our findings are consistent with those of previous experimental observations, which have also reported a change in magnetic properties from AFM to SDW-like fluctuations upon cooling.

Recent advancements in ultrafast laser systems and high harmonic generation (HHG) techniques have enabled time-resolved photoemission spectroscopy on femtosecond timescales, opening up unprecedented opportunities to explore quantum materials in both time and momentum space. In this review, we present recent representative studies utilizing HHG-laser-based time- and angle-resolved photoemission spectroscopy for a variety of quantum materials. We particularly highlight electron-phonon interactions and non-equilibrium dynamics in time and frequency domain, through which rich information about non-equilibrium electron-phonon couplings and related phenomena has been clearly revealed.

How proteins fold remains a central unsolved problem in biology. While the idea of a folding code embedded in the amino acid sequence was introduced more than 6 decades ago, this code remains undefined. While we now have powerful predictive tools to predict the final native structure of proteins, we still lack a predictive framework for how sequences dictate folding pathways. Two main conceptual models dominate as explanations of folding mechanism: the funnel model, in which folding proceeds through many alternative routes on a rugged, hyperdimensional energy landscape; and the foldon model, which proposes a hierarchical sequence of discrete intermediates. Recent advances on two fronts are now enabling folding studies in unprecedented ways. Powerful experimental approaches; in particular, single-molecule force spectroscopy and hydrogen (deuterium exchange assays) allow time-resolved tracking of the folding process at high resolution. At the same time, computational breakthroughs culminating in algorithms such as AlphaFold have revolutionized static structure prediction, opening opportunities to extend machine learning toward dynamics. Together, these developments mark a turning point: for the first time, we are positioned to resolve how proteins fold, why they misfold, and how this knowledge can be harnessed for biology and medicine.

Chiral molecules are known to preferentially select electrons with a particular spin state, an effect termed chirality-induced spin selectivity (CISS). In this work, the transient CISS dynamics in a chiral molecule are investigated through time-dependent quantum-transport simulations, an important step toward further understanding CISS and its application in devices such as magnetoresistive random access memories and spin-based quantum computers. We show that the spin-dependent group velocity of electrons is a possible contributor to a nonzero occupancy-based spin polarization throughout the chiral molecule. Contrary to the case which a chiral molecule is connected to a single lead, this spin polarization persists into the steady state when two leads are connected. We show that the simulated spin polarization qualitatively agrees with a reference experiment, as evidenced by the distinct magnetic-field signatures calculated from the spin polarization within a monolayer of chiral molecules.

One of the key challenges in performing muon experiments is knowing which temperatures and applied fields to measure at, and how many muon decays should be measured at each temperature/field combination to get the most useful dataset. We have developed a technique using Fisher information which, for a given muon asymmetry function, can analytically calculate the number of muon decays required to obtain a given error on the parameters of the asymmetry model. Here, we report on the results of our project, in particular applying our methodology to the problem of knowing the best choice of applied longitudinal fields for ionic diffusion experiments.

The practical implementation of lithium-metal anodes has been hindered by uncontrollable dendrite formation and interfacial instability. This study presents a defect-engineering approach of a chemically stable and electrically insulating interfacial layer of hexagonal boron nitride (h-BN) that markedly enhances ionic conductivity through argon ion irradiation. Initially, the electrochemical performance from commercially available, large-area chemical vapor deposition (CVD)-grown h-BN films with industrial-scale argon ion implantation motivated our subsequent detailed investigations using lab-scale exfoliated single-crystal h-BN flakes. Integration of these exfoliated flakes into a hybrid microfluidic-microelectronic chip provided direct evidence that controlled vacancy defects transform h-BN into an efficient lithium-ion conductor while preserving its intrinsic electrical insulation. Experimental validation confirmed improved lithium-metal anode stability, achieving dendrite-free cycling with Li plating/stripping Coulombic efficiencies exceeding 99.5% about 1000 cycles. Further assemble of irradiated h-BN in lithium-sulfur batteries effectively mitigates the polysulfide shuttle effect, sustaining over 97% specific capacity around 300 cycles. These results establish a robust, scalable interface-engineering route for next-generation lithium-metal batteries that combine high ionic transport with excellent electrical insulation.

Tin-based halide perovskites have emerged as promising lead-free alternatives for optoelectronic applications, yet their structural stability and phase behavior at finite temperatures remain challenging to predict. Here, we assess the predictive capabilities of the foundational machine learning model MACE-MP-0 - trained on a broad chemical space and applied without system-specific fine-tuning - for the temperature-dependent behavior of CsSnBr3 and Cs2SnBr6. Molecular Dynamics simulations in the NpT ensemble were performed from 100 K to 500 K, and thermodynamic and structural descriptors including enthalpy, specific heat, radial distribution functions, translational order, bond angle distributions, and vibrational spectra were analyzed. Our results show that CsSnBr3 undergoes a low-temperature orthorhombic-to-cubic phase transition, evidenced by both the evolution of lattice parameters and subtle anomalies in enthalpy and specific heat, although the experimentally observed intermediate tetragonal phase is not captured. In contrast, Cs2SnBr6 remains cubic and maintains a more rigid octahedral framework across the entire temperature range. Overall, MACE-MP-0 qualitatively reproduces key thermal and structural features of these materials, highlighting its usefulness as a first step for studying new materials. For cases where capturing more subtle phase behavior is required, system-specific fine-tuning with Density Functional Theory data should be considered.

Topological chiral crystals have emerged as a fertile material platform for investigating optical phenomena derived from the distinctive Fermi surface Berry curvature and orbital magnetic moment textures around multifold chiral band crossings pinned at the time-reversal invariant momenta. In this work, by means of tight-binding model and first principles based calculations, we investigate metallic electro-optic (EO) responses stemming from the Berry curvature and orbital magnetic moment of Bloch electrons across 37 materials belonging to space group 198 (SG198). Previously thought to vanish in SG198, our findings reveal a nonzero Berry curvature dipole attributed to the energetic misalignment between topologically charged point nodes of opposite chirality. Moreover, we find that the recently predicted magnetoelectric EO effects, which arise from the interplay between the Berry curvature and magnetic moment on the Fermi surface, are readily accessible in BeAu under experimentally feasible electric biases.

Hydrogen embrittlement remains a critical challenge in structural and electronic applications of copper (Cu) but its mechanism is still not fully understood. In this study, we combine density functional theory (DFT) and bond-order potential (BOP) simulations to determine the atomistic pathways for hydrogen adsorption/incorporation and fast interfacial diffusion at Cu grain boundaries (GBs), including its interaction with vacancies. Undercoordinated regions, such as surfaces and GBs, serve as preferential adsorption/incorporation sites for atomic hydrogen, especially in the presence of Cu vacancies. The presence of hydrogen in GB further enhances the segregation of Cu vacancies, leading to the formation of stable H-$V_\mathrm{Cu}$ complexes with cosegregation energy gains of up to $-0.8$ eV. Furthermore, our simulations reveal that the migration barriers for hydrogen within the GB networks are as low as $0.2$ eV and significantly lower than in bulk Cu ($0.42$ eV). The results presented in this paper suggest an atomistic mechanism that links $H_2$ exposure to H accumulation in GBs, providing information on the early stages of hydrogen-induced degradation.

Principal component analysis (PCA) is a powerful method that can identify patterns in large, complex data sets by constructing low-dimensional order parameters from higher-dimensional feature vectors. There are increasing efforts to use space-and-time-dependent PCA to detect transitions in nonequilibrium systems that are difficult to characterize with equilibrium methods. Here, we demonstrate that feature vectors incorporating the position and velocity information of driven skyrmions moving through random disorder permit PCA to resolve different types of disordered skyrmion motion as a function of driving force and the ratio of the Magnus force to the dissipation. Since the Magnus force creates gyroscopic motion and a finite Hall angle, skyrmions can exhibit a greater range of flow phases than what is observed in overdamped driven systems with quenched disorder. We show that in addition to identifying previously known skyrmion flow phases, PCA detects several additional phases, including different types of channel flow, moving fluids, and partially ordered states. Guided by the PCA analysis, we further characterize the disordered flow phases to elucidate the different microscopic dynamics and show that the changes in the PCA-derived order parameters can be connected to features in bulk transport measures, including the transverse and longitudinal velocity-force curves, differential conductivity, topological defect density, and changes in the skyrmion Hall angle as a function of drive. We discuss how asymmetric feature vectors can be used to improve the resolution of the PCA analysis, and how this technique can be extended to find disordered phases in other nonequilibrium systems with time-dependent dynamics.

Rydberg excitons in the semiconductor Cu$_2$O have been observed in absorption experiments up to a principal quantum number of n = 28 at millikelvin temperatures [1]. Here, we extend the experimental parameter space by variing both temperature and excitation power. In particular, we show that the P excitons close to the band gap react more sensitively to an increase of the excitation power than states of the associated D exciton multiplet, even though the latter are located at comparatively higher energy. This finding is similar to the one observed when applying an external electric field, suggesting that the observed behavior arises from internal electric fields created by charged impurities that are optically ionized. At laser intensities below 1 $\mu$W/cm$^2$, absorption lines of excitons with n=29 are observed.

Tunable Kondo lattice and heavy fermion physics have been recently reported in moiré materials, but most of the studies have focused on the electrical and magnetic properties. Quantitative thermoelectric measurements, which can reveal entropic information of the heavy fermions, have yet to be achieved. Here, we report a comprehensive thermoelectric study on the moiré heavy fermion phase realized in hole-doped angle-aligned MoTe2/WSe2 bilayers. By electrically gating the material to the Kondo lattice region of the phase diagram, we observe a sign change in the Seebeck coefficient near the Kondo coherence temperature, where the heavy fermion phase with an electron-like Fermi surface evolves into an itinerant Fermi liquid with a hole-like Fermi surface. We compare the results with the semiclassical Mott relation and discuss the observed discrepancies. In addition to the thermal dissociation of Kondo singlets in the heavy Fermi liquid, a sign change accompanied by a strong peak in the Seebeck coefficient is also observed near a Zeeman breakdown of the Kondo singlets, signaling an entropy accumulation. Our results provide entropic information on both the formation and breakdown of heavy fermions in moiré semiconductors.

Low-dimensional quantum systems host a variety of exotic states, such as symmetry-protected topological ground states in spin-1 Haldane chains. Real-world realizations of such states could serve as practical quantum simulators for quantum phases if the interactions can be controlled. However, many proposed models, such as the Affleck-Kennedy-Lieb-Tasaki (AKLT) state, require unconventional forms of spin interactions beyond standard Heisenberg terms, which do not naturally emerge from microscopic (Coulomb) interactions. Here, we demonstrate a general strategy to induce a biquadratic term between two spin-1 sites and to tune its strength $\beta$ by placing pairs of spin-1/2 spacers in between them. $\beta$ is controlled by the ratio of the Heisenberg couplings between the spin-1 sites and the spacer spins, and between the spacer spins themselves. Increasing this ratio increases the magnitude of $|\beta|$ and decreases the correlation length of edge states. Detailed atomistic calculations reveal that chains of nanographene flakes with 22 and 13 atoms, respectively, which could be realized by state-of-the-art bottom-up growth technology, yield precisely the couplings required to approach the AKLT state. These findings deliver a blueprint for engineering unconventional interactions in bottom-up synthesized quantum simulators.

Many protein-protein interaction (PPI) networks take place in the fluid yet structured plasma membrane. Lipid domains, sometimes termed rafts, have been implicated in the functioning of various membrane-bound signaling processes. Here, we present a model and a Monte Carlo simulation framework to investigate how changes in the domain size that arise from perturbations to membrane criticality can lead to changes in the rate of interactions among components, leading to altered outcomes. For simple PPI networks, we show that the activity can be highly sensitive to thermodynamic parameters near the critical point of the membrane phase transition. When protein-protein interactions change the partitioning of some components, our system sometimes forms out of equilibrium domains we term pockets, driven by a mixture of thermodynamic interactions and kinetic sorting. More generally, we predict that near the critical point many different PPI networks will have their outcomes depend sensitively on perturbations that influence critical behavior.

The Krylov subspace expansion is a workhorse method for sparse numerics that has been increasingly explored as source of physical insight into many-body dynamics in recent years. In this work we revisit the venerable Anderson model of localization in dimensions $d=1, 2, 3, 4$ to construct local integrals of motion (LIOM) in Krylov space. These appear as zero eigenvalue edge states of an effective hopping problem in the Krylov superoperator subspace and can be analytically constructed given the Lanczos coefficients. We exploit this idea, focusing on $d=3$, to study the manifestation of the disorder driven Anderson transition in the anatomy of LIOMs. We find that the increasing complexity of the Krylov operators results in a suppression of the fluctuations of the Lanczos coefficients. As such, one can study the phenomenology of the integrals of motion in the disorder averaged Krylov chain. We find edge states localized on vanishing fraction of Krylov space (of dimension $D_K=V^2$ for cubes of volume $V$), both in localized and extended phases. Importantly, in the localized phase, disorder induces powerlaw decaying dimerization in the (Krylov) hopping problem, producing stretched exponential decay of the LIOMs in Krylov space with a stretching exponent $1/2d$. Metallic LIOMs are completely delocalized albeit across only $\propto \sqrt{D_K}$ states. Critical LIOMs exhibit powerlaw decay with an exponent matching the expected value of $0.29$.

Electrically conducting polymers with mechanical adaptability are essential for flexible electronics, yet most suffer structural degradation under rapid deformation. In this study, multiscale coarse-grained (MSCG) simulations are used to uncover the nanoscale origins of an unusual strain-rate-dependent stiffening in a poly(2-acrylamido-2-methyl-1-propanesulfonic acid) (PAMPSA)-polyaniline (PANI) blend. The self-assembled morphology consists of semi-crystalline PANI-rich micellar cores dispersed in a soft, viscoelastic PAMPSA matrix. At low shear rates, micelles migrate and coalesce into larger aggregates, enhancing local crystallinity and transient entanglement density while dissipating stress through matrix deformation. At high shear rates, micelles cannot reorganize quickly enough, leading to core dissociation and the emergence of highly aligned PANI filaments that directly bear the load, with PAMPSA serving as a weak but extended support phase. These contrasting regimes (densification-driven local alignment versus dissociation-driven global alignment) enable reversible mechanical stiffening across three orders of magnitude in shear rate. The results provide a molecular-level framework for designing solid-state polymers with tunable, rate-adaptive mechanical properties.

Electron back-scatter diffraction (EBSD) has traditionally relied upon methods such as the Hough transform and dictionary Indexing to interpret diffraction patterns and extract crystallographic orientation. However, these methods encounter significant limitations, particularly when operating at high scanning speeds, where the exposure time per pattern is decreased beyond the operating sensitivity of CCD camera. Hence the signal to noise ratio decreases for the observed pattern which makes the pattern noisy, leading to reduced indexing accuracy. This research work aims to develop generative machine learning models for the post-processing or on-the-fly processing of Kikuchi patterns which are capable of restoring noisy EBSD patterns obtained at high scan speeds. These restored patterns can be used for the determination of crystal orientations to provide reliable indexing results. We compare the performance of such generative models in enhancing the quality of patterns captured at short exposure times (high scan speeds). An interesting observation is that the methodology is not data-hungry as typical machine learning methods.

Accurately describing the ground state of strongly correlated systems is essential for understanding their emergent properties. Neural Network Backflow (NNBF) is a powerful variational ansatz that enhances mean-field wave functions by introducing configuration-dependent modifications to single-particle orbitals. Although NNBF is theoretically universal in the limit of large networks, we find that practical gains saturate with increasing network size. Instead, significant improvements can be achieved by using a multi-determinant ansatz. We explore efficient ways to generate these multi-determinant expansions without increasing the number of variational parameters. In particular, we study single-step Lanczos and symmetry projection techniques, benchmarking their performance against diffusion Monte Carlo and NNBF applied to alternative mean fields. Benchmarking on a doped periodic square Hubbard model near optimal doping, we find that a Lanczos step, diffusion Monte Carlo, and projection onto a symmetry sector all give similar improvements achieving state-of-the-art energies at minimal cost. By further optimizing the projected symmetrized states directly, we gain significantly in energy. Using this technique we report the lowest variational energies for this Hamiltonian on $4\times 16$ and $4 \times 8$ lattices as well as accurate variance extrapolated energies. We also show the evolution of spin, charge, and pair correlation functions as the quality of the variational ansatz improves.

Symmetries strongly influence transport properties of quantum many-body systems, and can lead to deviations from the generic case of diffusion. In this work, we study the impact of time-reversal symmetry breaking on the transport and its universal aspects in integrable chiral spin ladders. We observe that the infinite-temperature spin transport is superdiffusive with a dynamical critical exponent z = 3/2 matching the one of the Kardar-Parisi-Zhang (KPZ) universality class, which also lacks the time reversal symmetry. However, we find that fluctuations of the net magnetization transfer deviate from the KPZ predictions. Moreover, the full probability distribution of the associated spin current obeys fluctuation symmetry despite broken time-reversal and space-reflection symmetries. To further investigate the role of conserved quantities, we introduce an integrable quantum circuit that shares the essential symmetries with the chiral ladder, and which exhibits analogous dynamical behaviour in the absence of energy conservation. Our work shows that time-reversal symmetry breaking is compatible with superdiffusion, but insufficient to stabilize the KPZ universality in integrable systems. This suggests that additional fundamental features are missing in order to identify the emergence of such dynamics in quantum matter.

We present a theoretical framework for investigating superconducting proximity effect in altermagnet (AM)-superconductor (SC) heterostructures. In general, AMs, characterized by vanishing net magnetization but spin-split electronic spectra, provide a promising platform for realizing unconventional magnetic phases. We consider a two-dimensional $d$-wave AM proximity coupled to a three dimensional ordinary $s$-wave SC. By integrating out the superconducting degrees of freedom, we derive an effective Hamiltonian that describes the proximity-induced modifications in the AM layer in the form of a self-energy. We then derive an effective Green's function to obtain the proximity-induced pairing amplitudes in the AM layer and classify the induced pairing amplitudes according to their parity, frequency, and spin. We find the presence of even-parity singlet and triplet pairing amplitudes in the AM layer. To achieve the odd-parity triplet components, important to realize topological superconductivity, we introduce a layer of Rashba spin-orbit coupling (RSOC) in the heterostructure. We analyse the band topology of this proximity-induced AM-RSOC layer and demonstrate the emergence of both weak and strong topological superconducting phases with edge-localized modes, characterized by winding number and Chern number. These findings highlight the role of AM-SC hybrid setup as a versatile platform for realizing odd-parity triplet pairings and engineering topological superconductivity in two-dimension.

Modern materials science generates vast and diverse datasets from both experiments and computations, yet these multi-source, heterogeneous data often remain disconnected in isolated "silos". Here, we introduce MaterialsGalaxy, a comprehensive platform that deeply fuses experimental and theoretical data in condensed matter physics. Its core innovation is a structure similarity-driven data fusion mechanism that quantitatively links cross-modal records - spanning diffraction, crystal growth, computations, and literature - based on their underlying atomic structures. The platform integrates artificial intelligence (AI) tools, including large language models (LLMs) for knowledge extraction, generative models for crystal structure prediction, and machine learning property predictors, to enhance data interpretation and accelerate materials discovery. We demonstrate that MaterialsGalaxy effectively integrates these disparate data sources, uncovering hidden correlations and guiding the design of novel materials. By bridging the long-standing gap between experiment and theory, MaterialsGalaxy provides a new paradigm for data-driven materials research and accelerates the discovery of advanced materials.

In the past decade, moiré materials have revolutionized how we engineer and control quantum phases of matter. Among incommensurate materials, moiré materials are aperiodic composite crystals whose long-wavelength moiré superlattices enable tunable properties without chemically modifying their layers. To date, nearly all reports of moiré materials have investigated van der Waals heterostructures assembled far from thermodynamic equilibrium. Here we introduce a conceptually new approach to synthesizing high-mobility moiré materials in thermodynamic equilibrium. We report a new family of foliated superlattice materials (Sr$_6$TaS$_8$)$_{1+\delta}$(TaS$_2$)$_8$ that are exfoliatable van der Waals crystals with atomically incommensurate lattices. Lattice mismatches between alternating layers generate moiré superlattices, analogous to those of 2D moiré heterobilayers, that are coherent throughout these crystals and are tunable through their synthesis conditions without altering their chemical composition. High-field quantum oscillation measurements map the complex Fermiology of these moiré metals, which can be tuned via the moiré superlattice structure. We find that the Fermi surface of the structurally simplest moiré metal is comprised of over 40 distinct cross-sectional areas, the most observed in any material to our knowledge. This can be naturally understood by postulating that bulk moiré materials can encode electronic properties of higher-dimensional superspace crystals in ways that parallel well-established crystallographic methods used for incommensurate lattices. More broadly, our work demonstrates a scalable synthesis approach potentially capable of producing moiré materials for electronics applications and evidences a novel material design concept for accessing a broad range of physical phenomena proposed in higher dimensions.

Cold-atom experiments which measure Fermi-gas properties near unitarity confine fermionic atoms to a region of space using trapping potentials of various shapes. The presence of a trapping potential introduces a new characteristic physical scale in the superfluid EFT which, inter alia, describes the acoustic branch of excitations in the far infrared well below the scale of the superfluid gap. In this EFT there is a clear hierarchy of scales, and corrections to the homogeneous system due to the trapping potential may be organized into three regions with distinct power counting that relies on both the EFT derivative expansion, and the WKB approximation, which is an expansion in gradients of the trapping potential. The energy spectrum of the superfluid system is obtained in each of the regions by explicit computation of the phonon-field fluctuations, and by the modifications to the dynamic structure factor due to the corresponding density fluctuations. The most significant deviations from linear dispersion due to the trapping potential are found in the far infrared region of the superfluid EFT.