Papers

We study the formation of hybrid plasmon-magnon modes in a heterostructure comprising a monolayer semiconductor with strong Rashba spin-orbit coupling – specifically, Janus transition-metal dichalcogenides (TMDs) – and an insulating ferrimagnet, such as yttrium iron garnet-based compounds. Using a combined microscopic-macroscopic framework for plasmon-magnon coupling, we show that plasmons and magnons strongly hybridize over both GHz and THz frequency ranges, enabling experimental access well above cryogenic temperatures. Moreover, the developed approach provides an efficient and natural classification of the topology of the hybrid modes, rooted in the phase winding of the plasmon-magnon coupling induced by spin-momentum locking and the associated chiral winding of the electronic spin along the Fermi contours. Finally, we identify experimentally accessible manifestations of the hybridization, such as topological interface modes and an anomalous thermal Hall response.

Magnetic impurities provide a route toward increasing functionality in electronic materials, often enabling new device concepts and architectures. In the case of topological semimetals, dilute magnetic doping presents a particularly attractive approach for inducing a Dirac to Weyl phase change via time reversal symmetry breaking. However, efforts to realize changes in the electronic structure have been limited by challenges in incorporating magnetic impurities into crystals with sufficiently high electron mobilities to detect them via transport or spectroscopic techniques. Here, we demonstrate incorporation of Mn into Cd$_3$As$_2$ Dirac semimetal thin films grown by molecular beam epitaxy (MBE). Using As-rich growth conditions and [001] oriented thin films, Mn compositions of >10% are achieved. Films contain uniform distributions of Mn with no evidence of secondary phases and exhibit electron mobilities greater than 10,000-30,000 cm$^2$/Vs up to 5% Mn. An evolution in the magnetization behavior along with the emergence of a second quantum oscillation frequency at low Mn concentrations provide preliminary evidence of Mn-induced changes in the electronic structure that are consistent with a Weyl phase. This work demonstrates the potential of magnetically doping topological semimetal thin films and a pathway for synthesizing them.

Flat-histogram Monte Carlo simulations are well-established, robust methods to perform random walks in a physical observable or parameter space, making them suitable for finding ground states or studying phase transitions in complex systems in statistical physics. However, their efficiency can be limited by the time to attain the desired flat distribution, which is generally unknown prior to the simulations. In particular, they might suffer from slowing down towards the end of a simulation due to the diffusive nature of random walks. In this work we apply irreversibility to the multicanonical Monte Carlo method via the lifting approach to alleviate this behavior. We achieve a 2-4 times speedup in ground-state search for a two-dimensional (2D) Ising model, and up to an order of magnitude of speedup for finding the ground-state energy in an Edwards-Anderson spin glass, compared to traditional multicanonical sampling. The round-trip times between ground states show a narrower distribution and are significantly shorter compared to the reversible counterpart, suggesting that a lower convergence time with a smaller time variance is feasible.

Realizing high-throughput aberration-corrected Scanning Transmission Electron Microscopy (STEM) exploration of atomic structures requires rapid tuning of multipole probe correctors while compensating for the inevitable drift of the optical column. While automated alignment routines exist, conventional approaches rely on serial, gradient-free searches (e.g., Nelder-Mead) that are sample-inefficient and struggle to correct multiple interacting parameters simultaneously. Conversely, emerging deep learning methods offer speed but often lack the flexibility to adapt to varying sample conditions without extensive retraining. Here, we introduce a Multi-Objective Bayesian Optimization (MOBO) framework for rapid, data-efficient aberration correction. Importantly, this framework does not prescribe a single notion of image quality; instead, it enables user-defined, physically motivated reward formulations (e.g., symmetry-induced objectives) and uses Pareto fronts to expose the resulting trade-offs between competing experimental priorities. By using Gaussian Process regression to model the aberration landscape probabilistically, our workflow actively selects the most informative lens settings to evaluate next, rather than performing an exhaustive blind search. We demonstrate that this active learning loop is more robust than traditional optimization algorithms and effectively tunes focus, astigmatism, and higher-order aberrations. By balancing competing objectives, this approach enables "self-optimizing" microscopy by dynamically sustaining optimal performance during experiments.

We introduce the idea that the P vs NP problem can have a finer structure. Given the NP complete problem of interest, the configurations space of the problem can be divided in (at least) two regions. In one region, polynomial algorithms to solve the NP complete problem of interest are available (and we discuss one possible realization inspire by the games of chess and go). In the second region the problem to find polynomial time algorithms is very similar to the problem to find polynomial time algorithms to determine the asymptotic behavior of discrete dynamical systems in the chaotic regime. We cannot exclude the existence of a third region which separates the first two: this region would have the characteristics of the edge of chaos. We focuss on the Wang tiling problem of an N X N square (with N large): here a Wang tiles set Gamma is an alphabet. We construct a statistical-physics inspired heuristic which allows to define good alphabets as the ones with a good thermodynamical behavior. For (a suitable subclass of) good alphabets we construct an algortihm which, in polynomial time, determines how to tile the N x N square. On the other hand, for bad alphabets, we observe a chaotic behavior. The Cook-Levin theorem advocate a similar pattern for all the NP-complete problems.

Moiré patterns in magnetic bilayers generate spatially modulated interlayer exchange interactions that can give rise to nonuniform magnetic textures. We study a minimal classical bilayer Ising model with a moiré-modulated interlayer coupling, generated either by relative twist or differential strain between the layers. Using large-scale classical Monte Carlo simulations, we show that the ordering transition remains in the conventional two-dimensional Ising universality class, even when the low-temperature state is domain-textured. At low temperatures, we find a smooth crossover between a uniform ferromagnet and domain-textured state, in which the spins locally follow the sign of the interlayer exchange. We demonstrate that there is no breaking of layer symmetry for twisted bilayers. The location of the crossover is determined by a simple geometric energy balance between bulk interlayer exchange and intralayer domain-wall costs. Our results provide a minimal framework for understanding how moiré-modulated magnetic textures can emerge from geometric energetics without requiring a thermodynamic phase transition.

Many challenges arising in Quantum Technology can be successfully addressed using a set of machine learning algorithms collectively known as reinforcement learning (RL), based on adaptive decision-making through interaction with the quantum device. After a concise and intuitive introduction to RL aimed at a broad physics readership, we discuss the key ideas and core concepts in reinforcement learning with a particular focus on quantum systems. We then survey recent progress in RL in all relevant areas. We discuss state preparation in few- and many-body quantum systems, the design and optimization of high-fidelity quantum gates, and the automated construction of quantum circuits, including applications to variational quantum eigensolvers and architecture search. We further highlight the interactive capabilities of RL agents, emphasizing recent progress in quantum feedback control and quantum error correction, and briefly discuss quantum reinforcement learning as well as applications to quantum metrology. The review concludes with a discussion of open challenges – such as scalability, interpretability, and integration with experimental platforms – and outlines promising directions for future research. Throughout, we highlight experimental implementations that exemplify the increasing role of reinforcement learning in shaping the development of quantum technologies.

Probabilistic bits (p-bits) have recently been employed in neural networks (NNs) as stochastic neurons with sigmoidal probabilistic activation functions. Nonetheless, there remain a wealth of other probabilistic activation functions that are yet to be explored. Here we re-engineer the p-bit by decoupling its stochastic signal path from its input data path, giving rise to a modular p-bit that enables the realization of probabilistic neurons (p-neurons) with a range of configurable probabilistic activation functions, including a probabilistic version of the widely used Logistic Sigmoid, Tanh and Rectified Linear Unit (ReLU) activation functions. We present spintronic (CMOS + sMTJ) designs that show wide and tunable probabilistic ranges of operation. Finally, we experimentally implement digital-CMOS versions on an FPGA, with stochastic unit sharing, and demonstrate an order of magnitude (10x) saving in required hardware resources compared to conventional digital p-bit implementations.

We analyze the quantum states of an isolated composite system consisting of two stacked quantum Ising (SQI) subsystems, coupled by a local Hamiltonian term that preserves the $Z_2$ symmetry of each subsystem. The coupling strength is controlled by an intercoupling parameter $w$, with $w=0$ corresponding to decoupled quantum Ising systems. We focus on the quantum correlations of one of the two SQI subsystems, $S$, in the ground state of the global system, and study their dependence on both the state of the weakly-coupled complementary part $E$ and the intercoupling strength. We concentrate on regimes in which $S$ develops critical long-range correlations. The most interesting physical scenario arises when both SQI subsystems are critical. In particular, for identical SQI subsystems, the global system is equivalent to the quantum Ashkin-Teller model, characterized by an additional $Z_2$ interchange symmetry between the two subsystem operators. In this limit, one-dimensional SQI systems exhibit a peculiar critical line along which the length-scale critical exponent $\nu$ varies continuously with $w$, while two-dimensional systems develop quantum multicritical behaviors characterized by an effective enlargement of the symmetry of the critical modes, from the actual $Z_2\oplus Z_2$ symmetry to a continuous O(2) symmetry.

Water electrolysis is an eco-friendly method for hydrogen production that has reached significant levels of technological maturity. Among commercialized water-electrolysis technologies, proton-exchange membrane electrolyzers offer high current density, fast dynamic response, and compact system design, among other advantages. On the other hand, managing their high capital cost and the “forever-chemistry” nature of Nafion, a perfluorinated proton-exchange membrane widely used in such devices, remains a major challenge. Searches for fluorine-free replacements for Nafion, pursued largely through physical experimentation, have been active for decades with limited success. In this work, we develop and demonstrate an AI-based strategy for designing proton-exchange membranes for electrolyzers. Two key components of this strategy are an implementation of the virtual forward-synthesis approach and a set of machine-learning predictive models for essential application-inspired membrane properties; the former generates a vast space of millions of synthesizable polymers, which are then evaluated and screened by the latter. The strategy is validated against experimental data for known membranes and then applied to design over 1700 synthesizable candidates. This article concludes with a forward-looking vision in which the strategy could be elevated into an interactive and iterative scheme that is based on large language models to facilitate materials design in multiple ways.

This work investigates the propagation of elastic waves in periodic Kelvin-cell chains, focusing on symmetry-breaking geometric modifications induced by twisting the cell's faces. By imposing such twists, the original lattice topology is preserved, while mirror symmetries are strategically broken through modifying a single geometric parameter, allowing wave characteristics to be adjusted without additional resonators or mass augmentation. The complex-valued Bloch-Floquet analysis reveals that twisting activates two distinct wave attenuation mechanisms: Bragg-type band gaps associated with periodicity-induced scattering, and polarization-dependent band gaps arising from longitudinal-torsional mode coupling and avoided crossings. To obtain qualitative and quantitative insight into these mechanisms, a simplified analytical model with coupled translational and rotational degrees of freedom is considered. The finite-element wave transmission calculations are experimentally validated on SLA-printed three-cell specimens, for which wave attenuation reaches up to 20 dB within the predicted band-gap frequencies. Note that high prediction accuracy requires accounting for viscoelastic material behavior, underscoring the importance of material behavior on the wave propagation characteristics. Overall, the findings show that modest geometric modifications to a classical Kelvin-cell lattice can enhance wave-filtering behavior, offering a tractable design strategy for vibration control in lightweight architected lattices.

We investigate and compare two particle number-conserving protocols for the preparation of a topologically nontrivial state. The first is derived from thermally coupling the system to a cold bath, while the second is based on engineered dissipation. We numerically study the time required to reach the target state as well as its robustness against physically important perturbations. Crucially, in both protocols, the cooling capability is limited by dissipatively induced diffusion processes. The resulting quadratic scaling of the cooling time with system size is also corroborated analytically using mean-field approximations and a purely classical random-walk model. Furthermore, we find that the engineered protocol admits a unique and stable dark state, which contributes to an ongoing discussion regarding the applicability of dissipative state preparation to many-body systems.

Random quantum states drawn from the Haar ensemble with a constraint on the energy expectation value $E_{\mathrm{av}} = \langle \psi | H | \psi\rangle$ display eigenstate condensation: for $E_{\mathrm{av}}$ below a critical value $E_c$, they develop macroscopic overlap with the ground state. We study eigenstate condensation in systems with finite-dimensional Hilbert spaces. These systems display three phases: a ground-state phase, in which energy-constrained random states have macroscopic overlap with the ground state; a high-temperature phase, in which they have exponentially small overlap with each energy eigenstate; and an anti-ground-state phase, in which they have macroscopic overlap with the most highly excited state. In local spin systems the ground-state and anti-ground-state phases approach the middle of the spectrum as $1/[\text{system size}]$, but – because the condensation phase transitions have exponential, rather than polynomial, finite-size scaling – the crossover becomes exponentially sharp in system size and the high-temperature phase is best understood as an extended phase.

Identifying superconducting states of matter without prior assumptions is a central challenge in strongly correlated electron systems. We introduce a canonical framework for diagnosing the formation of Cooper pair condensates based on the Penrose-Onsager criterion, in which superconducting order is encoded in the spectral properties of the two-particle reduced density matrix (2RDM). Within this formulation, the symmetry and structure of the condensate are obtained by projecting the 2RDM onto irreducible representations of the underlying symmetry group, enabling an unbiased identification of both conventional and exotic superconducting states. We demonstrate the power and versatility of the approach through applications to the two-dimensional Hubbard model, using both auxiliary-field quantum Monte Carlo (AFQMC) and the density matrix renormalization group (DMRG). For attractive interactions without a magnetic field, we reveal a clear finite-size scaling of the condensate fraction on square lattices of size up to $20\times 20$. The framework further provides direct access to the internal structure and extent of Cooper pairs, which we track across the BCS-BEC crossover. Moreover, it enables a clean diagnosis of the finite-momentum Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) phase in a magnetic field. Finally, we apply the approach to a supersolid phase in the repulsive Hubbard model with an additional next-nearest neighbor hopping $t^\prime$, where a charge-density wave coexists with a superconductor. We confirm the fragmented nature of the condensate and uncover substantial pairing correlations in the triplet channel with $p$-wave spatial symmetry in addition to the dominant singlet $d$-wave pairing. Our results establish the 2RDM-based Penrose-Onsager framework as a broadly applicable and unbiased tool for characterizing superconducting order in correlated quantum matter.

The Jordan-Wigner transformation connects spin operators in one-dimensional spin systems and fermionic operators. In this work, we elucidate the relationship between the finite-size corrections in the spin representation and the fermionic Casimir effect in the corresponding fermion representation. In particular, we focus on the ground-state energy of one-dimensional transverse-field Ising and XY models, and show that all finite-size corrections can be interpreted as lattice fermionic Casimir effects. We further find several types of Casimir phenomena, such as the conventional Casimir energy from massless fields, damping behavior from massive fields, vanishing behavior from flat or nonrelativistic bands, and oscillating behavior from the finite-density effect. Our findings establish a dictionary between finite-size corrections in spin chains and fermionic Casimir effects, and provide experimentally relevant platforms for the fermionic Casimir phenomena.

Predicting how organic molecules adsorb, assemble, and interact on metal surfaces is central to surface chemistry and molecular electronics, particularly in the context of interpreting high-resolution scanning probe microscopy. Yet, the application of first-principles simulations to interfaces is hampered by the computational cost for evaluating the electronic structure for the large number of atoms typically involved. We hereby present MAD-SURF, a machine learning interatomic potential specifically tailored for molecular adsorption on coinage metal surfaces. Trained on a broad dataset spanning diverse molecules, adsorption motifs, surfaces, molecular dynamics trajectories and non-covalent aggregates, MAD-SURF achieves accuracy comparable to the underlying DFT reference while enabling simulations orders of magnitude faster than density functional theory. The model reliably reproduces energies, forces and adsorption geometries across the three coinage metal substrates. We demonstrate its capabilities on experimentally characterized systems, including organic monolayers, polycyclic aggregates, flexible biomolecules and the long-range herringbone reconstruction of gold. By merging accuracy, speed, and generalizability, MAD-SURF offers a practical framework for accelerating atomistic simulations and advancing data-driven workflows in surface science.

We develop a simple and general variational method to estimate the solutions of the Gross-Pitaevskii equations and obtain the corresponding density profiles for all spin states of a trapped spin-1 Bose-Einstein condensate. We further employ this approach to obtain the complete phase diagram of the system under quasi-one-dimensional harmonic confinement, with ferromagnetic or antiferromagnetic spin interactions. We identify a suitable scaling that collapses all phase diagrams for different system sizes (i.e., total particle number) into a universal (system size-independent) phase diagram. The complete phase diagram for a confined system shows some significant qualitative differences compared to that of a condensate with homogeneous density distribution. The phase diagrams reported here could help identify the important parameter regimes in which phase transitions in the confined system, in general, occur. This knowledge of the region of phase boundaries can enable a reliable investigation of the instabilities near the boundaries that drive phase transitions.

Accurately simulating long-time dynamics of many-body systems is a challenge in both classical and quantum computing due to the accumulation of Trotter errors. While low-order Trotter-Suzuki decompositions are straightforward to implement, their rapidly growing error limits access to long-time observables. We present a framework for constructing efficient high-order Trotter-Suzuki schemes by identifying their structure and directly optimizing their parameters over a high-dimensional space. This method enables the discovery of new schemes with significantly improved efficiency compared to traditional constructions, such as those by Suzuki and Yoshida. Based on the theoretical efficiency and practical performance, we recommend two novel highly efficient schemes at $4^{\textrm{th}}$ and $6^{\textrm{th}}$ order. We also demonstrate the effectiveness of these decompositions on the Heisenberg model and the quantum harmonic oscillator, and find that for a fixed final time they perform better across the computational cost. Even when using large time steps, they surpass established low-order schemes like the Leapfrog. Finally, we investigate the in-practice performance of different Trotter schemes and find the decompositions with more uniform coefficients tend to feature improved error accumulation over long times. We have included this observation into our choice of recommended schemes.

Magnetic skyrmions are topological quasiparticles potentially useful for memory and computing devices. Antiferromagnetic (AF) skyrmions present no transverse deflection, making them suitable candidates for data storage applications. After the discovery of skyrmions with length scales comparable to the lattice constant, several works presented quantum analogues of classical ferromagnetic skyrmions in spin systems. However, studies about quantum analogues of AF skyrmions are still lacking. Here, we explore the phases of the AF quantum spin-1/2 Heisenberg model with Dzyaloshinskii-Moriya interactions on the triangular lattice using the density matrix renormalization group (DMRG) algorithm. We study the magnetization profile, spin structure factor and quantum entanglement of the resulting ground states to characterize the corresponding phases and signal the emergence of quantum AF skyrmions. Moreover, we present calculations of the quantum analogs of the scalar chirality, and construct local chirality maps, to further explore the different phases. Our results support that three-sublattice quantum antiferromagnetic skyrmion textures are stabilized in a wide range of magnetic fields.

We establish an exponentially decaying upper bound on the average energy that can be extracted in quantum energy teleportation (QET) protocols executed on finite-range {gapped} lattice systems possessing a unique ground state. Under mild regularity assumptions on the Hamiltonian and uniform operator-norm bounds on the local measurement operators, there exist positive constants $C$ and $\mu$ (determined by the spectral gap, interaction range and local operator norms) such that for any local measurement performed in a region $A$ and any outcome-dependent local unitaries implemented in a disjoint region $B$ separated by distance $d=\operatorname{dist}(A,B)$ one has $|E_A-E_B|\le C\,e^{-\mu d}$. The bound is nonperturbative, explicit up to model-dependent constants, and follows from the variational characterization of the ground state combined with exponential clustering implied by the spectral gap. We emphasize that the constants deteriorate as the gap closes (equivalently, as the correlation length diverges), so the estimate is intended for the gapped regime.

One-dimensional kagome strip chains share much of the same frustrated structural motif as two-dimensional kagome antiferromagnets, making them valuable for deepening our understanding of kagome lattice magnetism. In this paper, we report the hydrothermal synthesis and detailed structural and property characterization of Na2Co3(AsO4)2(OH)2, a striped kagome system. The crystal structure was characterized using single crystal X-ray diffraction, which reveals that Na2Co3(AsO4)2(OH)2 crystallizes in the monoclinic crystal system C2/m. The structure features a one-dimensional kagome strip lattice built from Co2+ ions and undergoes an antiferromagnetic transition at TN = 14 K. The magnetic ground state at zero field was characterized using neutron powder diffraction. Below the magnetic transition, Na2Co3(AsO4)2(OH)2 orders into an antiferromagnetic structure with a k-vector (0.5, 0.5, 0.5). In the proposed model, the Co1 moment is predominantly confined to the ac-plane while the Co2 moment is primarily aligned along the b-axis. Two flat bands were observed in the inelastic neutron spectra below the magnetic transition at 5 and 10 meV. Inelastic neutron spectra were modeled with a Heisenberg Hamiltonian including three nearest-neighbor exchange interactions (J1, J2, J3) and strong single-ion anisotropy to stabilize the observed magnetic structure. Our study highlights the complexity of the Co2+-based kagome strip magnetic lattice compound Na2Co3(AsO4)2(OH)2, which provides an excellent platform to broaden our understanding of the frustrated kagome magnetic lattice space.

Pump-probe experiments performed on $\mathrm{K}_3\mathrm{C}_{60}$ have unveiled both optical and transport signatures of metastable light-induced superconductivity up to room temperature, far above $T_c$. Recent experiments have uncovered that excitation in the vicinity of $50 ~\textrm{meV}$ enables the observation of high temperature light-induced superconductivity at significantly lower fluences. Inspired by these experiments we develop a mechanism which can explain such a giant resonant enhancement of light-induced superconductivity. Within a minimal non-linear Holstein model, we show that resonantly driving optical Raman modes leads to a time-dependent electron-phonon coupling. Such a coupling then modulates the effective electron-electron attraction, with the strongest modulations occurring when the drive is resonant with the phonon frequency. These dynamical modulations of the pairing interactions lead to Floquet-BCS instabilities at temperatures far exceeding equilibrium $T_c$, as observed in experiments. We conclude by discussing the implications of our general analysis on the $\mathrm{K}_3\mathrm{C}_{60}$ experiments specifically and suggesting experimental signatures of our mechanism.

In this letter we study renormalization group (RG) flows between 2d conformal field theories enjoying extended higher-spin $\mathcal{W}$-symmetry. We propose a new class of RG flows between the diagonal minimal models of $\mathcal{W}_N$-algebra that take the form $\mathcal{W}_N(p,q)\to\mathcal{W}_N(p,kp-q)$. These are obtained by matching the anomalies of the non-invertible symmetry ${\mathrm{Rep}}[SU(N)_{p-N}]$ (and its discrete quotients) that is preserved by special relevant primary fields. This large non-invertible symmetry includes the familiar $\mathbb{Z}_N$ symmetry of the minimal models. Our new flows furnish a significant generalization of the ones recently found in the case of Virasoro algebra, and include all previously known RG flows of $\mathcal{W}_N$. They have the remarkable property of being uniform in the rank $N$ of the $\mathcal{W}$-algebra.

McSAS3 is the refactored successor to the original McSAS Monte Carlo small-angle scattering analysis software. It is intended to be integrated in automated data processing pipelines, but can also be used to process individual (batches of) scattering data. McSAS3 comes with a graphical user interface (McSAS3GUI), complete with guides, examples and videos. McSAS3GUI will help to generate and test the three configuration files that McSAS3 needs for data read-in, Monte Carlo optimization and histogramming. The user interface can also be used to process individual files or batches, and can be augmented with machine-specific use templates. The Monte Carlo (MC) approach is able to fit most practical scattering patterns extremely well, resulting in form-free model parameter distributions. Theoretically, these can be distributions on any model parameter, but in practice the MC-optimized parameter is usually a (volume-weighted) size distribution, in absolute volume fraction for absolute-scaled data.

We study Langevin dynamics of $N$ Brownian particles on two-dimensional Riemannian manifolds, interacting through pairwise potentials linear in geodesic distance with quenched random couplings. These frustrated Brownian particles experience competing demands of random attractive and repulsive interactions while confined to curved surfaces. We consider three geometries: the sphere $S^2$, torus $T^2$, and bounded cylinder. Our central finding is disorder-induced dimension reduction with spontaneous rotational symmetry breaking: order emerges from two sources of randomness (thermal noise and quenched disorder), with manifold topology determining the character of emerging structures. Glassy relaxation drives particles from 2D distributions to quasi-1D structures: bands on $S^2$, rings on $T^2$, and localized clusters on the cylinder. Unlike conventional symmetry breaking, the symmetry-breaking direction is not frozen but evolves slowly via thermal noise. On the sphere, the structure normal precesses diffusively on the Goldstone manifold with correlation time $\tau_c \approx 18$, a classical realization of type-A dissipative Nambu-Goldstone dynamics. The model requires no thermodynamic gradients, no fine-tuning, and no slow external input. We discuss connections to spin glass theory, quantum field theory, astrophysical structure formation, and self-organizing systems. The model admits a large-$N$ limit yielding statistical field theory on Riemannian surfaces, while remaining experimentally realizable in colloidal and soft matter systems.

The fuzzy sphere regularization provides a powerful framework for studying three-dimensional (3D) conformal field theories (CFTs) by mapping them onto numerically tractable lattice models on the spherical lowest Landau level. However, the system sizes accessible to this method have been limited by the exact diagonalization (ED). In this work, we transcend this limitation by combining the fuzzy sphere regularization with a sophisticated multi-target density matrix renormalization group (DMRG) algorithm. Focusing on the 3D Ising-type model on the spherical lowest Landau level, we calculate the 24 low-lying energies at a larger system size than previously feasible with ED. At criticality, we extract the scaling dimensions of six primary operators, and the results show significantly improved agreement with bootstrap benchmarks compared to previous ED results at smaller sizes. Our approach allows us to efficiently target multiple excited states in larger systems beyond the reach of exact diagonalization. This study establishes the fuzzy sphere regularization combined with advanced DMRG techniques as a powerful and general framework for precision physics in 3D CFTs.

Superconducting systems may display different types of nonequilibrium states depending on the specific constraints imposed for measurement. We probe current-voltage relations of three-dimensional superconducting films by allowing finite voltages to develop across their length. Our experiments reveal sharp features of negative differential conductance which highlight the validity of the principle of minimum entropy production at the critical current transition. We have observed dissipative states with resistances intermediate between those of superconducting and normal phases at zero applied magnetic field, indicating a phenomenon of phase coexistence under nonequilibrium conditions. The features of steady states reported here are not accessible in conventional transport experiments with current-biasing methods.

Motivated by the objective of quantifying the energetic cost of accessing boundary phases through local control, we investigate here a simple, analytically tractable quantum impurity model. This model exhibits a rich boundary phase diagram, characterized by phases with different numbers of edge states. By considering a local quench protocol that drives the system out of equilibrium, we calculate exactly the resulting quantum work distribution across these phases. Our results show that the presence of edge states strongly alters this distribution. In particular, we analytically determine key fingerprints of these states both near the low-energy threshold and in the high-energy region.

The origin of wear particles in metallic sliding contacts remains debated. Classical views based on cold-welded junctions suggest that plastic yielding of the real contact area should lead to large wear coefficients, in apparent contradiction with the small values typically measured for metals. Here we argue that this discrepancy can be resolved if most junctions do not directly produce wear particles, but instead cause metal transfer and the formation of a weakly bound transfer film. Wear then occurs intermittently when fragments of this film detach due to crack propagation at the interface between the transfer film and the underlying bulk metal. We perform unlubricated reciprocating sliding experiments on nominally smooth stainless steel, brass, and aluminum. For steel on steel, the wear mass loss shows an initial stage with negligible mass change up to a sliding distance of $\sim 2.4 \ {\rm m}$, followed by a linear regime. Transfer-film formation in dissimilar-metal contacts is evidenced by optical imaging, net mass gain of the steel slider, and energy-dispersive X-ray spectroscopy, and the collected debris is flake-like. These observations support a transfer-film-controlled wear mechanism associated with cold-welded junctions.

Aliphatic hydrocarbons, such as normal alkanes, constitute a naturally abundant source of carbon atoms. Of special interest is the formation of cyclic and aromatic products from aliphatic reactants. Combining scanning tunneling microscopy and ab initio calculations, we investigate the thermal induced aromatization of linear n octane molecules on the catalytic Pt(111) surface and the reactions of intermolecular homocoupling between them at temperatures above 600 K. The cycloaromatization of individual n octane molecules requires bending the linear adsorbates prior to their dehydrogenation and the formation of an intramolecular C-C bond, yielding adsorbed benzene rings. In addition, the Pt(111) surface catalyzes a homocoupling reaction by initiating the formation of a C-C bond between the dehydrogenated methyl ends of the chemisorbed n octane molecules and then propagating along the carbon backbone in a zipper like fashion. Our findings provide molecular level insight into the heterogeneous catalytic processes underlying the generation of aromatic products and stable on surface polycyclic species.