Papers

Organic mixed ionic electronic conductors (OMIECs) are a promising class of polymer materials for applications spanning neuromorphic computation to energy efficient electronics and bioelectronics. Despite being highly tunable, the relationship between structural features and key performance properties such as charge carrier mobility is poorly understood. Scanning nanodiffraction in the transmission electron microscope (TEM) is a powerful probe for elucidating this structure-property relationship, but produces large, noisy datasets that are difficult to interpret because polymer reflections exhibit several distinct morphologies. To address the complexity, we trained a machine learning (ML) model to detect these polymer diffraction peaks and their intensities from synthetic data. Compared to correlative peak detection algorithms, the conventional method for analyzing nanobeam 4D scanning transmission electron microscopy (4DSTEM) data, we show that the ML model is significantly faster and outperforms correlative algorithms in almost all cases, opening up the possibility of near-live visualization of 4DSTEM experiments.

Signal dissipation in large-scale neural networks can lead to information loss and ultimately to computational failures, necessitating local signal amplification at neuron - synapse connections. In biological nervous systems, axons are responsible for local signal amplification. Translating axon functionality into hardware, i.e., the ability to amplify and transmit signals without loss, is non-trivial because emulating the human brain implies building networks composed of ~10 billion interconnected neurons, each requiring a dedicated compact and scalable amplifier. Here, we demonstrate signal amplification in simple two-terminal devices made of a metal-insulator transition material. By operating the devices on the verge of the phase transition and taking advantage of negative differential resistance, we achieve robust signal amplification up to a factor of ~11.5. We also demonstrate the amplification of spiking sequences generated by a real neuristor, opening new exciting opportunities for the direct integration of artificial neurons and axons. The amplification can be controlled by easily adjustable experimental parameters, including DC bias, AC excitation, series resistance, and temperature. We further propose a model that predicts the gain using readily observable transport characteristics. Our results establish a framework for developing and optimizing axon-like amplification functionalities in nonlinear electronic materials.

We solve the two models for Glauber dynamics and in equilibrium, both in the presence and absence of the external magnetic field on a complete graph. We compare the steady state of the Glauber dynamics with equilibrium and find that for low values of variance $R^2$ of the Gaussian random field, the steady state of the Glauber dynamics depends on the initial state. Beyond a critical value $R_{c}$ the equilibrium and non equilibrium steady states coincide. The variance $R$ in random field models behaves similar to the temperature. The location of both the continuous and first order transitions can be obtained exactly for the Glauber dynamics steady state. The frustration is introduced by considering repulsive bi-quadratic interaction for Blume-Emery-Griffiths model. We also consider repulsive bi-quadratic interaction and show that $R_c$ can become zero depending on the value of the crystal field. Interestingly, we also find that even when a system has $R_c=0$ at the start of quasi-static evolution with Glauber dynamics, with increasing $R$, in some regime of the couplings, the model undergoes a crossover to a random field Ising model universality with $R_c$ changing from $0$ to $\sqrt{\frac{2}{\pi}}$. In the presence of uniform magnetic field, regions of first order transition exhibit hysteresis under Glauber dynamics. These models exhibit rectangular, hexagonal, parallelogram, wasp-waisted, and double hysteresis loops. We derive the shapes of hysteresis loops analytically and also the equation for the coercive field. We show that while the area under the hysteresis loop depends on $R$, the shape is determined by the behavior of the models at $R=0$. In particular, in the case of Blume-Emery-Griffiths model the hysteresis plots have regions of continuous and first order transitions both, resulting in a rich phase diagram that depends non-trivially on the initial state.

Microscopic motors are commonly monitored through a single mechanical coordinate, whereas the chemical, conformational, or rotational cycles that sustain their motion remain unresolved. Mechanical stall is defined by the vanishing of the mean observed velocity; this scalar condition, however, does not generally imply thermodynamic reversibility. We analyze an overdamped Brownian motor in which one observed coordinate is coupled to several internal phases through a single shared interaction potential. Because the transmitted force and the reaction torques derive from the same energy, the resulting rank-one reciprocal structure yields an exact pointwise identity between the observed and hidden probability currents. This identity provides an exact expression for the entropy production at stall for an arbitrary periodic interaction. In particular, a positive current-square dissipation in the mechanical channel, defined from the full stationary state through the coupling-coordinate-resolved local velocity, determines together with the stall load the collective hidden current projected onto the coupling direction. The stationary one-coordinate marginal current nevertheless vanishes identically at stall; consequently, the stationary marginal density and mean displacement current do not determine this dissipation. Hidden currents orthogonal to the coupling direction dissipate without affecting the observed motion and generate an irreducible contribution that cannot be inferred from the rank-one mechanical channel.

An alternative explanation is presented for recent reports that attribute sustained chemiluminescence (CL) and electrochemiluminescence (ECL) from electrogenerated microbubbles on steel or copper electrodes in aqueous luminol solutions (over 2-30 V range) to the spontaneous formation of hydroxyl radicals at the gas-water interface. Our experiments with a broad set of electrodes, viz., steel, copper, aluminium, and platinum, reveal that while microbubbles can be electrogenerated on all electrodes, CL is exhibited by steel and Cu only and not by Al and Pt. These observations establish that the gas-water interface of microbubbles is not the site for hydroxyl radical generation (else CL would be recorded in all cases). Complementary quantification of H2O2 in these experiments reveals its electrode dependence as follows: Al > Cu > Steel > Pt. This establishes that depending on the electrode, H2O2 forms first, and in some cases, hydroxyl radicals are observed (i.e., where CL/ECL is seen). Experiments with NMR and EPR spectroscopy revealed that: (i) H2O2 formation occurs only when O2 is present in water; and (ii) while steel and copper generate hydroxyl radicals through 1-electron reduction of H2O2, Al does not promote one-electron reduction of H2O2 to generate hydroxyl radicals, and Pt preferentially promotes disproportionation of H2O2 to H2O and O2. In fact, we demonstrate that H2O2 and hydroxyl radicals can be observed at specific metal-water interfaces even without microbubbles, confirming that the solid surface is the reactive site. Therefore, this work affords electrode-based predictions of whether or not electrogenerated microbubbles would yield CL in luminol solutions and calls into question the notion of spontaneous formation of hydroxyl radicals at gas-water interfaces.

Accurate prediction of crystal properties remains a key challenge in computational materials science. While graph neural networks (GNNs) such as CGCNN, MEGNet, ALIGNN, and SchNet have shown strong performance, they primarily represent crystals at the atomic level and implicitly learn local chemical environments through message passing. However, many material properties are governed by coordination polyhedra, the fundamental structural units formed by atoms and their neighboring atoms. To address this limitation, we propose the Coordination Polyhedron Graph Network (CPGN), a multi-scale GNN that jointly learns atomic, bond, and coordination-polyhedron representations. CPGN constructs three coupled graphs: an atom graph encoding elemental and bonding information, a line graph capturing angular interactions, and a coordination polyhedron graph describing Voronoi-derived local environments through corner-, edge-, and face-sharing relationships. Physically meaningful geometric descriptors are incorporated for each polyhedron, while an interleaved message-passing mechanism with bidirectional cross-attention enables effective information exchange across structural levels. Extensive evaluations on the Materials Project, JARVIS-DFT, and QM9 benchmark datasets demonstrate that CPGN outperforms existing state-of-the-art GNN models. It achieves a formation-energy MAE of 0.060 eV/atom and a band-gap MAE of 0.292 eV on the Materials Project, while providing competitive multi-property prediction on JARVIS-DFT and superior HOMO prediction on QM9. The results highlight that explicit modeling of coordination polyhedra improves crystal representation learning and enables accurate, physically interpretable prediction of material properties.

Most AI agents act only after scientists have defined the task. Discovery is harder under practical uncertainties: the probe may not be where it is expected, the signal may occupy only a small region of a disordered sample, and the evidence may not distinguish among competing explanations. Here we show that an AI agent can decide what evidence an uncertain experiment needs next, and act on it. Beyond automation, AIMS, an uncertainty-aware experimentalist for cryogenic microwave impedance microscopy, quantifies uncertainty where it originates, in perception, sampling, and interpretation, and converts each into its own corrective action rather than a single confidence score. Given only an open objective, AIMS relocated a probe lost during cooldown while flagging its own unreliable estimates, mapped twist angle disorder to locate the strongest correlated states in twisted bilayer MoSe$_2$, and uncovered a paradox: the half-filled stripe that classical theory predicts should melt first survived longest. Distinguishing an incomplete model from a wrong mechanism, AIMS commissioned a beyond-mean-field calculation and an independent structural measurement as the decisive tests, revising its interpretation as each arrived: quantum motion reverses the classical hierarchy, stabilizing the half-filled stripe while destabilizing its neighbors. These uncertainty-to-action loops are generic to scanning probe experiments, and AIMS turns uncertainty from an obstacle into a driver of discovery.

We report robust Fano-like spectral profiles in substrate-supported Au-coated Ag nanoislands fabricated by solid-state dewetting followed by Au overgrowth. Combining electrostatic modal analysis, full-wave simulations, and linear transmission spectroscopy, we investigate the origin of these profiles, their tunability, and their robustness against morphological disorder. Increasing the Au coating thickness progressively red-shifts the low-energy resonance and transfers spectral weight to it from the high-energy resonance, whereas the Fano-like antiresonant dip undergoes a limited spectral displacement. The electrostatic modal analysis identifies this dip as the signature of destructive interference between superradiant and subradiant hybridized modes, and its weak spectral evolution is consistently reproduced by theory, simulations, and experiments. These results establish Au-coated Ag nanoislands as a scalable, lithography-free platform for engineering robust yet tunable Fano-like optical responses accessible through standard far-field spectroscopy.

We study the two-step Floquet dynamics of the chiral $J_1$-$J_2$-$K$ Heisenberg model on the $4\times4$ torus, alternating its non-chiral Heisenberg part $H_{AF}$ and its chiral plaquette part $H_K$, at a parameter point where the static model hosts a quasi-degenerate, spectrally isolated chiral-spin-liquid (CSL) topological doublet. The one-period propagator is computed exactly in all symmetry sectors. Decreasing the frequency, the doublet survives the drive far beyond the frequency $\omega_{res}\simeq 11.5 J_1$ at which folded states first cross it in quasienergy: the time-averaged energy of the Floquet eigenstates, which orders the folded spectrum, shows that the doublet remains the isolated bottom of the spectrum down to $\omega\simeq 6J_1$, while stroboscopic time evolution over thousands of periods shows no heating for $\omega\gtrsim\omega_{res}$ and only slow absorption below. The quasienergy resonances of the folded regime are invisible in the average energy, identifying them as parametrically weak avoided crossings. We argue that this mechanism, stability controlled by local energy scales rather than by the extensive many-body bandwidth, is of the type expected to survive in the thermodynamic limit, where a prethermal Floquet CSL should persist for $\omega$ above a threshold set by local scales, with heating times exponentially long in $\omega/J_1$, a conjecture that larger clusters can now test. Consistently, the optimal $D=3$ chiral PEPS of the static problem still describes the driven doublet deep in the folded regime, with an essentially unchanged local tensor. Finally, we show that the drive is realizable on existing quantum simulators, via an explicit digital pulse sequence of XY-type two-spin gates on the four bond colours of the square lattice, with an infidelity of $1.5\times10^{-5}$ per period.

Superconductor-semiconductor Josephson junction arrays are a uniquely tunable platform for studying collective quantum phenomena, particularly in the regime where localized Andreev bound states can hybridize across the lattice when the physical separation between adjacent junctions is smaller than their coherence length ($\xi_{\text{ABS}}>d_{\text{JJ}}$). Here, we investigate three distinct Al-InAs Josephson junction arrays: a square array and super-honeycomb array fabricated within this ${\xi_{\text{ABS}}>d_{\text{JJ}}}$ regime, as well as a larger-spacing super-honeycomb control device designed such that $\xi_{\text{ABS}}\lesssim\!~d_{\text{JJ}}$. Under an out-of-plane field, critical current peaks emerge at rational filling factors, reflecting stable vortex configurations in the lattices. In the super-honeycomb lattice, vortices localize to distinct non-identical plaquettes at different filling factors, as predicted by frustrated XY model simulations. A rotating in-plane field yields periodic critical current oscillations that reflect the Rashba spin-orbit coupling inherent to the InAs quantum well. Surprisingly, at $f = 1$, the closely spaced super-honeycomb array exhibits a distinct critical current minimum as the magnitude of the in-plane field increases, a signature absent in the square array and large-spacing super-honeycomb array. These results indicate that this signature is jointly influenced by the unique geometry of the super-honeycomb vortex lattice and by long-range inter-junction hybridization.

Physical vapor deposition enables the fabrication of nanostructured superalloys with unique defect architectures, yet their phase evolution pathways can differ significantly from those of conventionally processed alloys. In this study, the effects of solution and aging treatments on phase selection and precipitation behavior in sputtered Inconel 725 films with an initially uniform columnar nanotwinned structure were systematically investigated. Direct aging at relatively low temperatures promoted extensive {\delta}-phase precipitation at twin boundaries and defect-rich regions, which depleted Nb from the {\gamma} matrix and suppressed {\gamma}'/{\gamma}" precipitation. In contrast, high-temperature solution treatment induced recrystallization and eliminated the nanotwinned structure, significantly reducing {\delta}-phase precipitation and increasing Nb availability to enable the formation of ultrafine spherical {\gamma}'/{\gamma}" precipitates within a refined {\gamma} matrix (<1 {\mu}m). Subsequent aging treatments promoted elemental partitioning and drove the morphological evolution of {\gamma}'/{\gamma}" precipitates from spherical to lenticular forms, while {\delta} precipitation became increasingly concentrated along grain boundaries. This spatial separation of intragranular {\gamma}'/{\gamma}" and grain-boundary {\delta} phases enabled simultaneous precipitation strengthening and grain stabilization, resulting in hardness values approaching 9 GPa. As a whole, this study demonstrates that the initial templates provided by defect structures can govern phase selection and precipitation pathways, providing a strategy for tailoring microstructure and achieving synergistic strengthening in nanostructured superalloys.

The high efficiency of cluster expansions make them appealing for studying chemical disorder in complex composition spaces such as in multi-principal element alloys (MPEAs). Several works have attempted to address the rapidly growing training cost with number of chemical species through active learning, transfer learning, and chemical embedding. However, many composition spaces have large regions where the host lattice becomes dynamical unstable, which are often avoided a priori so as to not generate expensive but inapplicable training data. Here, we demonstrate a procedure for integrating stability classification within an active learning workflow to autonomously avoid calculations for unstable structures. Our workflow augments the stability classification procedure with Mahalanobis distance-based structure selection to ensure model robustness by training set diversification. We benchmark our methods by training a cluster expansion for the complex FCC MPEA spanning the Ni-Fe-Cr-Al-Ti-Si alloy space, in which only Ni and Al are thermodynamically stable as FCC

Bond reconstruction in vacancy-related structures affects their formation energies, symmetries, and electronic and optical properties. Using density functional theory, we investigate bond reconstruction mechanisms of monovacancies and vacancy aggregates in monolayer silicon carbide. Multiple bond descriptors reveal that isolated monovacancies undergo both in-plane reconstruction and out-of-plane distortion, which together shape their stability and electronic structure. For compact vacancy aggregates, we show that bond reconstruction acts as a key stabilization mechanism. However, in carbon monovacancy, reconstruction suppresses optical activity. In contrast, a highly stable aggregate composed of three carbon vacancies surrounding a silicon vacancy emerges as a promising infrared color-center candidate, combining a triplet ground state with a favorable Debye-Waller factor of the emission. These results highlight the role of bond reconstruction in defining the quantum properties of vacancy defects in two-dimensional silicon carbide.

Machine-learning detection of phase transitions depends not only on the learning algorithm, but also on whether the input representation preserves the symmetries of the system. We examine this for the weak first-order isotropic–nematic transition of the three-dimensional Lebwohl–Lasher model, whose apolar and continuously degenerate nematic phase makes raw molecular configurations a challenging input for unsupervised learning. Principal component analysis (PCA) and a convolutional autoencoder (CAE) fail to identify the transition from raw configurations because rotationally equivalent nematic states can appear far apart in the input space. When the same configurations are transformed into a rotationally invariant local-correlation representation, both methods recover transition-sensitive signatures and bimodal coexistence distributions. A supervised three-dimensional convolutional neural network (CNN), by contrast, accurately predicts the scalar order parameter from raw configurations when given order-parameter labels. The Lebwohl-Lasher model therefore separates unsupervised phase discovery from supervised order-parameter regression and shows that symmetry-respecting input representations are needed for unsupervised machine learning in orientationally ordered systems.

Stochastic resetting has evolved from a simple model of diffusive search acceleration into a general framework for predicting, inferring, and controlling stochastic dynamics far from equilibrium. Its defining features, i.e., the creation of non-equilibrium steady states and the acceleration of first-passage kinetics, are increasingly relevant across physical chemistry, from biological restart mechanisms to molecular simulations and colloidal experiments. We review the renewal theory underlying stochastic resetting and show how it enables prediction of reset dynamics from properties of the underlying process, while also allowing the latter to be inferred from the resetting-accelerated dynamics. We then discuss applications to state preparation, enhanced sampling, kinetic inference, and training and sampling of machine learning models. Finally, we review recent advances in adaptive resetting, environmental feedback, many-body dynamics, and thermodynamic costs of resetting. These developments establish new opportunities for controlling stochastic dynamics with resetting across theory, simulations, and experiments.

The buckling of elastic filaments in viscous fluids, ubiquitous in biological systems like flagella, microtubules, and DNA, has long been described by the static Euler-elastica. Yet, when such filaments buckle dynamically, their shapes defy static predictions, exhibiting complex, unpredictable behaviours. Here, we use a coarse-grained numerical model to explore the long-timescale dynamics of filament buckling in Stokes flow, revealing three distinct morphological regimes, termed flip, loop, and knot. The dominance of each regime is primarily governed by the dimensionless buckling number $\mathrm{Bu}$. Fourier analysis shows that these transitions between shape regimes arise from competition between the first three curvature modes, with high-order modes decay fitting an exponential law. In some parameter ranges, distinct shapes coexist for close initial conditions, indicating deterministic sensitivity to small perturbations. These findings bridge static and dynamic buckling theories, with implications for biological propulsion and the design of microscale slender swimmers.

We derive the amplitude of quantum-beats in the fluorescence from geminate triplet-exciton fusion in rubrene and tetracene from the full set of parameters that characterize triplet exciton dynamics. We find that the amplitude depends on the fission time in tetracene, but does not do so in rubrene, where it is determined by the dimensionality of triplet transport. Kinetic Monte Carlo simulations reproduce the experimental data in both materials, for a fission time of the order of 200 ps and isotropic triplet transport int tetracene, and for a triplet hopping time along the herringbone axis of 250 ps and anisotropic transport in rubrene.

Understanding how structural relaxation evolves from equilibrium to nonequilibrium conditions remains a central problem in glass physics. Using simulations of model glass formers under steady shear, we show that the String Model, which links stringlike cooperative rearrangements to relaxation in equilibrium glass-forming liquids, predicts the structural relaxation time across the full range of temperatures and shear rates investigated without additional nonequilibrium fitting parameters, when combined with a shear-dependent effective temperature that is captured by a generalized fluctuation-dissipation relation. These results indicate that equilibrium and nonequilibrium relaxation are governed by the same underlying cooperative mechanism, but they occur under different effective thermodynamic conditions under steady shear. More broadly, our study provides a unified microscopic description of thermal and mechanically driven dynamics in glass-forming liquids.

The deliberate breaking of inversion ($\mathcal{P}$) symmetry in antiferromagnets has recently emerged as an effective means to induce various features, such as the emergence of Berry curvature, spin-valley locking, magnetoelectric coupling, and the transition from conventional antiferromagnetism to altermagnetism. Conversely, in non-magnetic systems, inversion symmetry breaking in the presence of strong spin-orbit interaction (SOI) gives rise to momentum-dependent spin splitting via the Rashba effect, enabling tunable spin polarization through external electric fields. Motivated by recent advances in two-dimensional materials, we perform first-principles calculations based on density functional theory to investigate the van der Waals (vdW) heterostructure formed by a $\mathcal{P}$-symmetric MnPS$_3$ monolayer and a WS$_2$ monolayer. We demonstrate that the interface hosts a rich interplay of emergent phenomena, including an altermagnetic phase, Rashba spin splitting, spin-valley locking, and valley polarization. Our results demonstrate that the heterostructure exhibits semiconducting behavior with a direct band gap of approximately 1.65~eV and a type-I band alignment. Remarkably, the electronic structure and band alignment can be effectively tuned between type-I and type-II regimes via an external electric field and in-plane biaxial strain. Furthermore, field-induced modulation enables strong control over the altermagnetic phase and the valley splitting. These findings establish the proposed vdW heterostructure as a highly tunable platform with significant potential for spintronic and valleytronic applications.

Self-assemblies of tunable units are being intensively studied as physical systems with signal-processing capabilities. Specifically, silver nanowire networks (AgNWNs) have demonstrated accumulation, non-linearity, and memory retention with multiple timescales, features that enable a wide variety of neuromorphic implementations. In this study, we aim to extract the interconnection scheme to analyze the experimentally obtained network architecture and, eventually, use it as input to a previously developed simulation platform. By post-processing photomicrographs of AgNWN, we present a pipeline optimized to extract the interconnection diagram, recognizing the intersections formed among the nanowires, to determine the associated graph for each physical sample. A graph is a collection of nodes and edges whose properties can be associated with different electrical responses. It is thus possible to study graphs' metrics, such as the degree distribution, community size, clustering coefficient, and path-length, to compare the experimental assemblies' attributes to those of topological models of reference. Small-world, modular, and scale-free are well-known structures in the field of mathematical graphs. By analyzing the degree distribution, the adjacency matrices, among other useful representation means, the experimental assemblies reveal similarities to both small-world and modular topologies. All the mentioned analyses were conducted considering the interconnection scheme obtained from zenithal-view optical images, which overestimates the number of NWs' interconnections (due to the impossibility of distinguishing real junctions from spurious cross-points between vertically displaced NWs). For that reason, this communication also studies the impact of artificially removing junctions from the resulting graphs on the previously calculated clustering coefficient and path length.

Scanning tunneling microscopy studies of periodic arrays of triple steps fabricated on single-crystalline Si(5 5 7) wafers demonstrate several possible atomic structures of consecutive steps and Si(1 1 1) terraces maintaining the same periodicity on micrometer-sized surface areas. Detailed analysis of the atomically resolved data reveals the formation of Si(8 8 11) triple-step staircase with a period of 18b=5.99 nm in projection onto the terrace plane, where b=0.333 nm is the distance between atomic rows for the Si(1 1 1)1x1 surface. Schematic models for several possible configurations of either 7x7 or 5x5-reconstructed terraces and triple steps are proposed.

The study of electric fields in soft materials converges either to the study of point-like particles (local) in nonlinear theories or to the use of particles with finite sizes in nonlocal linear theories. In this work, we start from the microscopic equations of motion and construct a unified mean field Fokker-Planck equation that describes the non-equilibrium electrostatics of nonlocal nonlinear systems. We obtain a generalized Poisson-Boltzmann equation for such systems, as well as their electrostatic free energy expression. In the linear approximation, we obtain an anisotropic susceptibility in an isotropic fluid, which allows for local linear response inversion in electrostatics.

Altermagnetism in the case of local-moment magnets is characterized by zero net magnetization and splitting of opposite chirality magnon bands in the absence of any spin-anisotropic interactions or external field. In this work we investigate the robustness of altermagnets against quantum fluctuations arising from magnetic frustration. We consider a Heisenberg model on a square lattice with two different types of second-neighbor interactions, as in a checkboard pattern, in addition to the nearest-neighbor interaction. This model continuously interpolates between the $J_{1}-J_{2}$ Heisenberg model on the square lattice and the Heisenberg model on the checkerboard lattice. For weaker second-neighbor interactions a Néel-type altermagnet phase is realized. On the other hand, for stronger second-neighbor interactions a columnar antiferromagnet emerges. Using linear spin-wave theory we calculate the magnon dispersion, order parameter, static and dynamical structure factors for the two magnetic phases. Further, we show that there is an intermediate quantum disordered phase separating the two magnetic phases, which is connected to the one realized in the $J_{1}-J_{2}$ model. The quantum disordered region is further stabilized as we tune towards the checkerboard lattice limit.

Diverging timescales are the hallmark of critical dynamics and the bottleneck to their classical and quantum simulation. To tame this, we compress a spacetime tensor network: temporally into semigroup influence matrices and spatially via matrix-product states. Benchmarking on the two-impurity Anderson model with its four fermionic species, we compute spectral functions to map the evolution from a Kondo resonance, across a non-Fermi-liquid quantum critical point, and into a gapped singlet phase. Resolving transient through asymptotic dynamics in sudden quenches and Kibble-Zurek ramps, we obtain the universal two-channel-Kondo exponent. Together, these results establish multibath influence matrices as a practical tool for real-time dynamics in strongly correlated multi-orbital systems.

We show that no stabilizer state in a discrete realization of a local quantum field theory can flow in the continuum to the vacuum or to any state that resembles the vacuum at short distances. The argument rests on the fact that the entanglement spectrum is flat for stabilizer states but non-flat for cyclic and separating states in a local QFT as a consequence of the type III$_1$ nature of the von Neumann algebras associated with arbitrary subregions. Our result implies that simulating physically relevant QFT states necessarily requires quantum resources beyond stabilizer states and Clifford operations. We comment on the implications for holography.

Real-space geometric curvature in magnetic thin films introduces a controllable mechanism for tailoring the pathways of superconducting steady-state Josephson currents via the proximity effect. We present a generalized Green's function method for calculating diffusive transport in arbitrarily curved surfaces, and show how the competing mechanisms of curvature and distance regulate conversion between different superconducting pairings in a proximity-coupled ferromagnet. We show how this dictates the distribution of current density, with the possibility of curvature-controlled current density manipulation, induced eddy currents and current reversal.

Floquet driving underlies Hamiltonian and gate engineering, and produces dynamical orders with no equilibrium counterpart. These phenomena are, however, transient in well-isolated systems. We show that statically coupled dissipative auxiliaries cool the system toward low-energy states of the Floquet-engineered Hamiltonian, stabilizing orders in the steady state. The excess energy density is controllably small at high drive frequency and weak coupling and is captured by rate theory based on Fermi's Golden Rule. We numerically confirm robust cooling in a Floquet-engineered transverse-field Ising chain in both phases, and demonstrate discrete time-crystalline order, with a period-doubled magnetization response, in the steady state of a long-range Ising chain. Our results provide a rare analytical handle on the steady states of driven dissipative systems.

Efficient operation of neural networks has been linked to criticality in their underlying non-equilibrium excitation dynamics. However, obtaining experimental evidence of this conjecture remains challenging due to limited control and undersampling in biological systems. Here, we experimentally explore neural network criticality using an ultracold Rydberg gas as a highly controllable simulator. We highlight the similarity of the excitation spreading via Rydberg facilitation and the synaptic connection of spiking activity of neurons, giving rise to distinct absorbing and active phases. We systematically explore and resolve criticality criteria, including power-law scaling of excitation avalanches and the emergence of universal avalanche shape collapse. Crucially, we implement a controlled gain mechanism to compensate for atom loss, mimicking metabolic resource replenishment and stabilizing the system in a controlled non-equilibrium steady state. We find peak temporal correlations at the critical point and stochastic oscillations with dragon king avalanches in the active phase, consistent with predictions for systems orbiting criticality. Our work establishes facilitated Rydberg gases as a platform for investigating criticality, resource dynamics, and emergent oscillations in neural networks.

Maxwell demons can convert knowledge into thermodynamic advantage by using measurement-acquired information. However, standard formulations of this problem assume that the demon has access to the system's entire phase space, an assumption that fails in many practical applications. Here, I address this problem by deriving fluctuation relations for general feedback-controlled systems, including such singular cases. The central quantity is a generalized notion of unavailable information: a portion of the acquired information that cannot be used to extract useful work. Finally, a Szilard engine with finite resolution illustrates these results. In the high-resolution limit, the information acquired by the demon may diverge, yet the extractable work remains finite. This work shows that this occurs because an equally divergent amount of information becomes unavailable.

Ferroelectric materials are promising platforms for controllable photonic devices because of the strong coupling between their spontaneous polarization and optical properties. Yet, these materials remain challenging to design because of the close connection between the ferroelectric domain structure and optical response, which no existing theoretical approach can capture. Here, we develop a dynamical phase-field model that directly couples the ferroelectric order to the local optical response by introducing an electronic polarization field. This approach enables the prediction of the spatially resolved temperature- and wavelength-dependent optical properties in complex ferroelectric microstructures. Applying this method to BaTiO3 thin films, we investigate the evolution of the local refractive index and electro-optic response under varying electric fields and temperatures. We show that the ferroelectric domain structure strongly modifies the local electro-optic response, exceeding 4000 pm/V near domain walls, several times larger than the bulk single crystal value (r_51=1300 pm/V). Our simulations quantitatively reproduce the electro-optic coefficient measured in BaTiO3 on silicon films and capture the temperature-dependent behavior across multiple ferroelectric phase transitions, revealing the role of phase competition and coexistence in determining the electro-optic response. More broadly, this work establishes a general approach for predicting light-matter interactions in complex ferroelectric microstructures, enabling the computational design of ferroelectric materials for photonics.