The anti-de Sitter/conformal field theory (AdS/CFT) correspondence is a remarkable tool for analytically studying strongly coupled physics. Thanks to the AdS/CFT correspondence, strongly correlated quantum systems can be understood as weakly coupled gravity theories, which live in a holographic spacetime with an extra emergent dimension and constant negative curvature. Fundamental observables such as correlation functions are identified with scattering amplitudes in curved spacetime, which, in principle, can be computed by using standard perturbation theory. Unfortunately, such holographic calculations are notoriously difficult, even for tree-level processes involving four external particles. Despite relentless efforts over the past two decades, a full solution to this problem was not found. In this article, we introduce a powerful new method that solves this long-standing problem. We give a closed-form formula for all such four-point functions in a class of theories that constitute the best-known paradigms of AdS/CFT. These models exhaust the theories compatible with maximal supersymmetry, and they live in three, four, and six dimensions. Pivotal to our construction is the use of symmetries. We show that in a judiciously chosen limit, symmetry principles dictate a drastic simplification in holographic correlators, allowing them to be directly computed. Having solved this limit, we further show that the full correlators can be recovered from this special configuration by using only symmetries. In addition to providing valuable explicit expressions that have a wide range of applications in AdS/CFT, our analysis leads to several important conceptual lessons. Our results point out remarkable simplicities underlying the holographic correlators, as well as concrete ways to search for such structures. Moreover, our construction identifies previously unknown elegant underlying organizing principles for holographic correlators. These qualitative features of holographic correlators also echo the exciting progress in the scattering amplitude program in flat space, suggesting tantalizing prospects of future cross-fertilization of ideas.

We propose a novel nonequilibrium phenomenon, through which a prompt quench from a metal to a transient superconducting state can induce large oscillations of the order parameter amplitude. We argue that this oscillating mode acts as a source of parametric amplification of the incident radiation. We report experimental results on optically driven ${\mathrm{K}}_3{\mathrm{C}}_{60}$ that are consistent with these predictions. The effect is found to disappear when the onset of the excitation becomes slower than the Higgs-mode period, consistent with the theory proposed here. These results open new possibilities for the use of collective modes in many-body systems to induce nonlinear optical effects.

Charge profiles in liquid electrolytes are of crucial importance for applications such as supercapacitors, fuel cells, batteries, or the self-assembly of particles in colloidal or biological settings. However, creating localized (screened) charge profiles in the bulk of such electrolytes generally requires the presence of surfaces—for example, provided by colloidal particles or outer surfaces of the material—which poses a fundamental constraint on the material design. Here, we show that topological defects in nematic electrolytes can perform as regions for local charge separation, forming charged defect cores and, in some geometries, even electric multilayers, as opposed to the electric double layers found in isotropic electrolytes. Using a Landau-de Gennes-Poisson-Boltzmann theoretical framework, we show that ions highly effectively couple with the topological defect cores via ion solvability and with the local director-field distortions of the defects via flexoelectricity. The defect charging is shown for different defect types—lines, points, and walls—using geometries of ionically screened flat isotropic-nematic interfaces, radial hedgehog point defects, and half-integer wedge disclinations in the bulk and as stabilized by (charged) colloidal particles. More generally, our findings are relevant for possible applications where topological defects act as diffuse ionic capacitors or as ionic charge carriers.

The velocity circulation, a measure of the rotation of a fluid within a closed path, is a fundamental observable in classical and quantum flows. It is indeed a Lagrangian invariant in inviscid classical fluids. In quantum flows, circulation is quantized, taking discrete values that are directly related to the number and the orientation of thin vortex filaments enclosed by the path. By varying the size of such closed loops, the circulation provides a measure of the dependence of the flow structure on the considered scale. Here, we consider the scale dependence of circulation statistics in quantum turbulence, using high-resolution direct numerical simulations of a generalized Gross-Pitaevskii model. Results are compared to the circulation statistics obtained from simulations of the incompressible Navier-Stokes equations. When the integration path is smaller than the mean intervortex distance, the statistics of circulation in quantum turbulence displays extreme intermittent behavior due to the quantization of circulation, in stark contrast with the viscous scales of classical flows. In contrast, at larger scales, circulation moments display striking similarities with the statistics probed in the inertial range of classical turbulence. In particular, we observe the emergence of the power-law scalings predicted by Kolmogorov’s 1941 theory, as well as intermittency deviations that closely follow the recently proposed bifractal model for circulation moments in classical flows. To date, these findings are the most convincing evidence of intermittency in the large scales of quantum turbulence. Moreover, our results strongly reinforce the resemblance between classical and quantum turbulence, highlighting the universality of inertial-range dynamics, including intermittency, across these two a priori very different systems. This work paves the way for an interpretation of inertial-range dynamics in terms of the polarization and spatial arrangement of vortex filaments.

Determining the different conformational states of a protein and the transition paths between them is key to fully understanding the relationship between biomolecular structure and function. This can be accomplished by sampling protein conformational space with molecular simulation methodologies. Despite advances in computing hardware and sampling techniques, simulations always yield a discretized representation of this space, with transition states undersampled proportionally to their associated energy barrier. We present a convolutional neural network that learns a continuous conformational space representation from example structures, and loss functions that ensure intermediates between examples are physically plausible. We show that this network, trained with simulations of distinct protein states, can correctly predict a biologically relevant transition path, without any example on the path provided. We also show we can transfer features learned from one protein to others, which results in superior performances, and requires a surprisingly small number of training examples.

The probability of two loci, separated by a certain genome length, being in contact can be inferred using the chromosome conformation capture (3C) method and related Hi-C experiments. How to go from the contact map, a matrix listing the mean contact probabilities between a large number of pairs of loci, to an ensemble of three-dimensional structures is an open problem. A solution to this problem, without assuming an assumed energy function, would be the first step in understanding the way nature has solved the packaging of chromosomes in tight cellular spaces. We created a theory, based on polymer physics characteristics of chromosomes and the maximum entropy principles, referred to as HIPPS (Hi-C-polymer-physics-structures) method, that allows us to calculate the 3D structures solely from Hi-C contact maps. The first step in the HIPPS method is to relate the mean contact probability ($⟨p_{ij}⟩$) between loci $i$ and $j$ and the average spatial distance $⟨{\overline{r}}_{ij}⟩$. This is a difficult problem to solve because the cell population is heterogeneous, which means that a given contact exists only in a small unknown fraction of cells. Despite the population heterogeneity, we first prove that there is a theoretical lower bound connecting $⟨p_{ij}⟩$ and $⟨{\overline{r}}_{ij}⟩$ via a power-law relation. We show, using simulations of a precisely solvable model, that the overall organization is accurately captured by constructing the distance map from the contact map even if the cell population is highly heterogeneous, thus justifying the use of the lower bound. In the second step, the mean distance matrix, with elements $⟨{\overline{r}}_{ij}⟩\prime \mathrm{s}$, is used as a constraint in the maximum entropy principle to obtain the joint distribution of spatial positions of the loci. Using the two steps, we created an ensemble of 3D structures for the 23 chromosomes from lymphoblastoid cells using the measured contact maps as inputs. The HIPPS method shows that conformations of chromosomes are heterogeneous even in a single cell type. The differences in the conformational heterogeneity of the same chromosome in different cell types (normal as well as cancerous cells) can also be quantitatively discerned using our theory. We validate the method by showing that the calculated volumes of the 23 chromosomes from the predicted 3D structures are in good agreement with experimental estimates. Because the method is general, the 3D structures for any species may be calculated directly from the contact map without the need to assume a specific polymer model, as is customarily done.

The recent discovery of superconductivity in oxygen-reduced monovalent nickelates has raised a new platform for the study of unconventional superconductivity, with similarities to and differences from the cuprate high-temperature superconductors. In this paper, we investigate the family of infinite-layer nickelates $R{\mathrm{NiO}}_2$ with rare-earth $R$ spanning across the lanthanide series, introducing a new and nontrivial “knob” with which to tune nickelate superconductivity. When traversing from La to Lu, the out-of-plane lattice constant decreases dramatically with an accompanying increase of Ni $d_{x^2-y^2}$ bandwidth; however, surprisingly, the role of oxygen charge transfer diminishes. In contrast, the magnetic exchange grows across the lanthanides, which may be favorable to superconductivity. Moreover, compensation effects from the itinerant $5d$ electrons present a closer analogy to Kondo lattices, indicating a stronger interplay between charge transfer, bandwidth renormalization, compensation, and magnetic exchange. We also obtain the microscopic Hamiltonian using the Wannier downfolding technique, which will provide the starting point for further many-body theoretical studies.

An accurate description of the optical response of subwavelength metallic particles and nanogap structures is a key problem of plasmonics. Quantum hydrodynamic theory (QHT) has emerged as a powerful method to calculate the optical response of metallic nanoparticles (NPs) since it takes into account nonlocality and spill-out effects. Nevertheless, the absorption spectra of metallic NPs obtained with conventional QHT, i.e., incorporating Thomas-Fermi (TF) and von Weizsäcker (vW) kinetic energy (KE) contributions, can be affected by several spurious resonances at energies higher than the main localized surface plasmon (LSP). These peaks are not present in reference time-dependent density-functional-theory spectra, where, instead, only a broad shoulder exists. Moreover, we show here that these peaks incorrectly reduce the LSP peak intensity and have a strong dependence on the simulation domain size so that a proper calculation of QHT absorption spectra can be problematic. In this article, we introduce a more general QHT method accounting for KE contributions depending on the Laplacian of the electronic density ($q$), thus, beyond the gradient-only dependence of the TFvW functional. We show that employing a KE functional with a term proportional to $q^2$ results in an absorption spectrum free of spurious peaks, with LSP resonance of correct intensity and numerically stable Bennett state. Finally, we present a novel Laplacian-level KE functional that is very accurate for the description of the optical properties of NPs of different sizes as well as for dimers. Thus, the Laplacian-level QHT represents a novel, efficient, and accurate platform to study plasmonic systems.

Competition and collaboration are at the heart of multiagent probabilistic spreading processes. The battle for public opinion and competitive marketing campaigns are typical examples of the former, while the joint spread of multiple diseases such as HIV and tuberculosis demonstrates the latter. These spreads are influenced by the underlying network topology, the infection rates between network constituents, recovery rates, and, equally important, the interactions between the spreading processes themselves. Here, for the first time, we derive dynamic message-passing equations that provide an exact description of the dynamics of two, interacting, unidirectional spreading processes on tree graphs, and we develop systematic low-complexity models that predict the spread on general graphs. We also develop a theoretical framework for the optimal control of interacting spreading processes through optimized resource allocation under budget constraints and within a finite time window. Derived algorithms can be used to maximize the desired spread in the presence of a rival competitive process and to limit the spread through vaccination in the case of coupled infectious diseases. We demonstrate the efficacy of the framework and optimization method on both synthetic and real-world networks.

One of the most fundamental problems in quantum many-body physics is the characterization of correlations among thermal states. Of particular relevance is the thermal area law, which justifies the tensor network approximations to thermal states with a bond dimension growing polynomially with the system size. In the regime of sufficiently low temperatures, which is crucially important for practical applications, the existing techniques do not yield optimal bounds. Here, we propose a new thermal area law that holds for generic many-body systems on lattices. We improve the temperature dependence from the original $\mathcal{O}(\beta )$ to $\mathcal{O}({\beta }^{2/3})$ up to a logarithmic factor, thereby suggesting subballistic propagation of entanglement by imaginary-time evolution. This qualitatively differs from the real-time evolution, which usually induces linear growth of entanglement. We also prove analogous bounds for the Rényi entanglement of purification and the entanglement of formation. Our analysis is based on a polynomial approximation to the exponential function which provides a relationship between the imaginary-time evolution and random walks. Moreover, for one-dimensional (1D) systems with $n$ spins, we prove that the Gibbs state is well approximated by a matrix product operator with a sublinear bond dimension for $\beta =o[\log (n)]$. This proof allows us to rigorously establish, for the first time, a quasilinear time classical algorithm for constructing a matrix product state representation of 1D quantum Gibbs states at arbitrary temperatures of $\beta =o[\log (n)]$. Our new technical ingredient is a block decomposition of the Gibbs state that bears a resemblance to the decomposition of real-time evolution given by Haah et al. [Proceedings of the 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS) (IEEE, New York, 2018), pp. 350–360].

According to thermodynamics, the inevitable increase of entropy allows the past to be distinguished from the future. From this perspective, any clock must incorporate an irreversible process that allows this flow of entropy to be tracked. In addition, an integral part of a clock is a clockwork, that is, a system whose purpose is to temporally concentrate the irreversible events that drive this entropic flow, thereby increasing the accuracy of the resulting clock ticks compared to counting purely random equilibration events. In this article, we formalize the task of autonomous temporal probability concentration as the inherent goal of any clockwork based on thermal gradients. Within this framework, we show that a perfect clockwork can be approximated arbitrarily well by increasing its complexity. Furthermore, we combine such an idealized clockwork model, comprised of many qubits, with an irreversible decay mechanism to showcase the ultimate thermodynamic limits to the measurement of time.

Rubber elasticity is the archetype of the entropic force emerging from the second law of thermodynamics; numerous experimental and theoretical studies on natural and synthetic rubbers have shown that the elasticity originates mostly from entropy change with deformation. Similarly, in polymer gels containing a large amount of solvent, it has also been postulated that the shear modulus (the modulus of rigidity) $G$, which is a kind of modulus of elasticity, is approximately equivalent to the entropy contribution $G_S$, but this has yet to be verified experimentally. In this study, we measure the temperature dependence of the shear modulus $G$ in a rubberlike (hyperelastic) polymer gel whose polymer volume fraction is at most 0.1. As a result, we find that the energy contribution $G_E=G-G_S$ can be a significant negative value, reaching up to double the shear modulus $G$ (i.e., $|G_E|\simeq 2G$), although the shear modulus of stable materials is generally bound to be positive. We further argue that the energy contribution $G_E$ is governed by a vanishing temperature that is a universal function of the normalized polymer concentration, and $G_E$ vanishes when the solvent is removed. Our findings highlight the essential difference between rubber elasticity and gel elasticity (which were previously thought to be the same) and push the established field of gel elasticity into a new direction.

Controlling an evolving population is an important task in modern molecular genetics, including directed evolution for improving the activity of molecules and enzymes, in breeding experiments in animals and in plants, and in devising public health strategies to suppress evolving pathogens. An optimal intervention to direct evolution should be designed by considering its impact over an entire stochastic evolutionary trajectory that follows. As a result, a seemingly suboptimal intervention at a given time can be globally optimal as it can open opportunities for desirable actions in the future. Here, we propose a feedback control formalism to devise globally optimal artificial selection protocol to direct the evolution of molecular phenotypes. We show that artificial selection should be designed to counter evolutionary trade-offs among multivariate phenotypes to avoid undesirable outcomes in one phenotype by imposing selection on another. Control by artificial selection is challenged by our ability to predict molecular evolution. We develop an information theoretical framework and show that molecular timescales for evolution under natural selection can inform how to monitor a population in order to acquire sufficient predictive information for an effective intervention with artificial selection. Our formalism opens a new avenue for devising artificial selection methods for directed evolution of molecular functions.

Artificial model swimmers offer a platform to explore the physical principles enabling biological complexity, for example, multigait motility: a strategy employed by many biomicroswimmers to explore and react to changes in their environment. Here, we report bimodal motility in autophoretic droplet swimmers, driven by characteristic interfacial flow patterns for each propulsive mode. We demonstrate a dynamical transition from quasiballistic to bimodal chaotic propulsion by controlling the viscosity of the environment. To elucidate the physical mechanism of this transition, we simultaneously visualize hydrodynamic and chemical fields and interpret these observations by quantitative comparison to established advection-diffusion models. We show that, with increasing viscosity, higher hydrodynamic modes become excitable and the droplet recurrently switches between two dominant modes due to interactions with the self-generated chemical gradients. This type of self-interaction promotes self-avoiding walks mimicking examples of efficient spatial exploration strategies observed in nature.

Artificial spin ices (ASIs) are interacting arrays of lithographically defined nanomagnets in which novel, frustrated magnetic phases can be intentionally designed. A key emergent description of fundamental excitations in ASIs is that of magnetic monopoles—mobile quasiparticles that carry an effective magnetic charge. Here, we demonstrate that the archetypal square ASI lattice can host, in specific regions of its magnetic phase diagram, plasmalike regimes containing a high density of mobile magnetic monopoles. These regimes result from the magnetic field-tunable tension on the Dirac strings connecting mobile monopoles. By passively “listening” to spontaneous monopole noise under conditions of strict thermal equilibrium, we reveal their intrinsic dynamics and show that monopole kinetics are most diffusive (that is, minimally correlated) in the plasma regime. These results open the door to on-demand monopole regimes having continuously field-tunable densities and dynamic properties, thereby providing a new paradigm for probing the physics of effective magnetic charges in synthetic matter.

Topological superconductors (TSCs) are correlated quantum states with simultaneous off-diagonal long-range order and nontrivial topological invariants. They produce gapless or zero-energy boundary excitations, including Majorana zero modes and chiral Majorana edge states with topologically protected phase coherence essential for fault-tolerant quantum computing. Candidate TSCs are very rare in nature. Here, we propose a novel route toward emergent quasi-one-dimensional (1D) TSCs in naturally embedded quantum structures such as atomic line defects in unconventional spin-singlet $s$-wave and $d$-wave superconductors. We show that inversion symmetry breaking and charge transfer due to the missing atoms lead to the occupation of incipient impurity bands and mixed-parity spin-singlet and -triplet Cooper pairing of neighboring electrons traversing the line defect. Nontrivial topological invariants arise and occupy a large part of the parameter space, including the time-reversal symmetry-breaking Zeeman coupling due to applied magnetic field or defect-induced magnetism, creating TSCs in different topological classes with robust Majorana zero modes at both ends of the line defect. Beyond providing a novel mechanism for the recent discovery of zero-energy bound states at both ends of an atomic line defect in monolayer Fe(Te,Se) superconductors, the findings pave the way for new material realizations of the simplest and most robust 1D TSCs using embedded quantum structures in unconventional superconductors with large pairing energy gaps and high transition temperatures.

The identification of universal properties from minimally processed data sets is one goal of machine learning techniques applied to statistical physics. Here, we study how the minimum number of variables needed to accurately describe the important features of a data set—the intrinsic dimension ($I_d$)—behaves in the vicinity of phase transitions. We employ state-of-the-art nearest-neighbors-based $I_d$ estimators to compute the $I_d$ of raw Monte Carlo thermal configurations across different phase transitions: first-order, second-order, and Berezinskii-Kosterlitz-Thouless. For all the considered cases, we find that the $I_d$ uniquely characterizes the transition regime. The finite-size analysis of the $I_d$ allows us to not only identify critical points with an accuracy comparable to methods that rely on a priori identification of order parameters but also to determine the corresponding (critical) exponent $\nu$ in the case of continuous transitions. For the case of topological transitions, this analysis overcomes the reported limitations affecting other unsupervised learning methods. Our work reveals how raw data sets display unique signatures of universal behavior in the absence of any dimensional reduction scheme and suggest direct parallelism between conventional order parameters in real space and the intrinsic dimension in the data space.

Topological magnets are a new family of quantum materials providing great potential to realize emergent phenomena, such as the quantum anomalous Hall effect and the axion-insulator state. Here, we present our discovery that the stoichiometric ferromagnet ${\mathrm{MnBi}}_8{\mathrm{Te}}_{13}$ with natural heterostructure ${\mathrm{MnBi}}_2{\mathrm{Te}}_4/({\mathrm{Bi}}_2{\mathrm{Te}}_3{)}_3$ is an unprecedented “half-magnetic topological insulator,” with the magnetization existing at the ${\mathrm{MnBi}}_2{\mathrm{Te}}_4$ surface but not at the opposite surface terminated by triple ${\mathrm{Bi}}_2{\mathrm{Te}}_3$ layers. Our angle-resolved photoemission spectroscopy measurements unveil a massive Dirac gap at the ${\mathrm{MnBi}}_2{\mathrm{Te}}_4$ surface and a gapless Dirac cone on the other side. Remarkably, the Dirac gap (about 28 meV) at the ${\mathrm{MnBi}}_2{\mathrm{Te}}_4$ surface decreases monotonically with increasing temperature and closes right at the Curie temperature, thereby representing the first smoking-gun spectroscopic evidence of a magnetization-induced topological surface gap among all known magnetic topological materials. We further demonstrate theoretically that the half-magnetic topological insulator is desirable to realize the surface anomalous Hall effect, which serves as direct proof of the general concept of axion electrodynamics in condensed matter systems.