Imaging of nanoscale magnetic textures within extended material systems is of critical importance to both fundamental research and technological applications. While high-resolution magnetic imaging of thin nanoscale samples is well established with electron and soft x-ray microscopy, the extension to micrometer-thick systems currently requires hard x rays, which limits high-resolution imaging to rare-earth magnets. Here, we overcome this limitation by establishing soft x-ray magnetic imaging of micrometer-thick systems using the pre-edge phase x-ray magnetic circular dichroism signal, thus making possible the study of a wide range of magnetic materials. By performing dichroic spectroptychography, we demonstrate high spatial resolution imaging of magnetic samples up to $1.7\mathrm{\mu }\mathrm{m}$ thick, an order of magnitude higher than conventionally possible with soft x-ray absorption-based techniques. We demonstrate the applicability of the technique by harnessing the pre-edge phase to image thick chiral helimagnets, and naturally occurring magnetite particles, gaining insight into their three-dimensional magnetic configuration. This new regime of magnetic imaging makes possible the study of extended non-rare-earth systems that have until now been inaccessible, including magnetic textures for future spintronic applications, non-rare-earth permanent magnets for energy harvesting, and the magnetic configuration of giant magnetofossils.

Cells contain multiple condensates which spontaneously form due to the heterotypic interactions between their components. Although the proteins and disordered region sequences that are responsible for condensate formation have been extensively studied, the rule of interactions between the components that allow demixing, i.e., the coexistence of multiple condensates, is yet to be elucidated. Here, we construct an effective theory of the interaction between heteropolymers by fitting it to the molecular dynamics simulation results obtained for more than 200 sequences sampled from the disordered regions of human proteins. We find that the sum of amino acid pair interactions across two heteropolymers predicts the Boyle temperature qualitatively well, which can be quantitatively improved by the dimer pair approximation, where we incorporate the effect of neighboring amino acids in the sequences. The improved theory, combined with the finding of a metric that captures the effective interaction strength between distinct sequences, allowed the selection of up to three disordered region sequences that demix with each other in multicomponent simulations, as well as the generation of artificial sequences that demix with a given sequence. The theory points to a generic sequence design strategy to demix or hypermix thanks to the low-dimensional nature of the space of the interactions that we identify. As a consequence of the geometric arguments in the space of interactions, we find that the number of distinct sequences that can demix with each other is strongly constrained, irrespective of the choice of the coarse-grained model. Altogether, we construct a theoretical basis for methods to estimate the effective interaction between heteropolymers, which can be utilized in predicting phase separation properties as well as rules of assignment in the localization and functions of disordered proteins.

Numerous natural and human-made systems exhibit critical transitions whereby slow changes in environmental conditions spark abrupt shifts to a qualitatively distinct state. These shifts very often entail severe consequences; therefore, it is imperative to devise robust and informative approaches for anticipating the onset of critical transitions. Real-world complex systems can comprise hundreds or thousands of interacting entities, and implementing prevention or management strategies for critical transitions requires knowledge of the exact condition in which they will manifest. However, most research so far has focused on low-dimensional systems and small networks containing fewer than ten nodes or has not provided an estimate of the location where the transition will occur. We address these weaknesses by developing a deep-learning framework which can predict the specific location where critical transitions happen in networked systems with size up to hundreds of nodes. These predictions do not rely on the network topology, the edge weights, or the knowledge of system dynamics. We validate the effectiveness of our machine-learning-based framework by considering a diverse selection of systems representing both smooth (second-order) and explosive (first-order) transitions: the synchronization transition in coupled Kuramoto oscillators; the sharp decline in the resource biomass present in an ecosystem; and the abrupt collapse of a Wilson-Cowan neuronal system. We show that our method provides accurate predictions for the onset of critical transitions well in advance of their occurrences, is robust to noise and transient data, and relies only on observations of a small fraction of nodes. Finally, we demonstrate the applicability of our approach to real-world systems by considering empirical vegetated ecosystems in Africa.

Two-dimensional (2D) transition metal dichalcogenides (TMDs) is a versatile class of quantum materials of interest to various fields including, e.g., nanoelectronics, optical devices, and topological and correlated quantum matter. Tailoring the electronic properties of TMDs is essential to their applications in many directions. Here, we report that a highly controllable and uniform on-chip 2D metallization process converts a class of atomically thin TMDs into robust superconductors, a property belonging to none of the starting materials. As examples, we demonstrate the introduction of superconductivity into a class of 2D air-sensitive topological TMDs, including monolayers of ${\mathrm{T}}_{\mathrm{d}}\text{−}{\mathrm{WTe}}_2$, $1{\mathrm{T}}^{\prime }\text{−}{\mathrm{MoTe}}_2$, and $2\mathrm{H}\text{−}{\mathrm{MoTe}}_2$, as well as their natural and twisted bilayers, metallized with an ultrathin layer of palladium. This class of TMDs is known to exhibit intriguing topological phases ranging from topological insulator, Weyl semimetal to fractional Chern insulator. The unique, high-quality two-dimensional metallization process is based on our recent findings of the long-distance, non-Fickian in-plane mass transport and chemistry in 2D that occur at relatively low temperatures and in devices fully encapsulated with inert insulating layers. Highly compatible with existing nanofabrication techniques for van der Waals stacks, our results offer a route to designing and engineering superconductivity and topological phases in a class of correlated 2D materials.

Transportation and distribution networks are a class of spatial networks that have been of interest in recent years. These networks are often characterized by the presence of complex structures such as central loops paired with peripheral branches, which can appear both in natural and manmade systems, such as subway and railway networks. In this study, we investigate the conditions for the emergence of these nontrivial topological structures in the context of human transportation in cities. We propose a simple model for spatial networks generation, where a network lattice acts as a planar substrate and edge speeds define an effective temporal distance which we aim to optimize and quantifies the efficiency in exploring the urban space. Complex network topologies can be recovered from the optimization of edges’ speeds and we study how the interplay between a flow probability between two nodes in space and the associated travel cost influences the resulting optimal network. In the perspective of urban transportation we simulate these flows by means of human mobility models to obtain origin-destination matrices. We find that when using simple lattices, the obtained optimal topologies transition from treelike structures to more regular networks, depending on the spatial range of flows. Remarkably, we find that branches paired to large loops structures appear as optimal structures when the network is optimized for an interplay between heterogeneous mobility patterns of small range travels and longer-range ones typical of commuting. Moreover, when congestion dynamics in traffic routing is considered, we study the emergence of additional edges from the tree structure to mitigate temporal delays. Finally, we show that our framework is able to recover the statistical spatial properties of the Greater London area subway network.

Paramagnetic transition metals play a crucial role as cofactors in various cellular catalytic processes. However, their high concentrations in reactive oxidation states can induce oxidative stress, resulting in cell dysfunction or death. Hence, it is vital to have methods to monitor metal concentrations and paramagnetic properties in cells for medicine and cell biology. Here we present a novel multimodal method for in-cell magnetometry enabling direct measurement of metal magnetic properties within individual cells in tissue, without prior isolation and at room temperature. Individual cell magnetic moments are measured using superresolution magnetic resonance imaging (MRI) microscopy at 9.4 T by detecting microscopic magnetic-field perturbations around the cells. The cellular metal content is quantified using ion-beam microscopy or synchrotron micro-x-ray fluorescence for the same cells. The metal magnetic susceptibility at 9.4 T is then obtained from the slope of the cell magnetic moments’ dependence on cell metal content. To estimate the susceptibility at lower fields, multifield MR relaxometry and biophysical modeling are employed, extrapolating the 9.4-T susceptibility values to fields as low as 3 T. We apply the new method to determine the susceptibility of iron accumulated in human dopaminergic neurons inside neuromelanin, the by-product of dopamine synthesis. The susceptibility of iron in neuromelanin is measured to be ${\chi }_{\rho }=(2.98\pm 0.19)\times {10}^{-6}{\mathrm{m}}^3/\mathrm{kg}$ providing unique insights into the biochemistry of iron inside dopaminergic neurons. The obtained value reveals a predominant monoatomic low-affinity iron-binding site within neuromelanin, indicating a higher neurotoxicity of iron than previously suggested. Furthermore, the measured susceptibility value establishes a quantitative relationship between cellular iron concentration and iron-sensitive MRI parameters, which can be noninvasively measured in vivo. This breakthrough paves the way for the in vivo detection of dopaminergic neuron density and iron load, requiring a standard clinical MRI scanner only. It promises to facilitate early diagnosis of Parkinson’s disease. In conclusion, our presented novel method enables the direct measurements of magnetic properties of paramagnetic metals within single cells with high sensitivity and across large cell groups within a macroscopic volume, providing invaluable information about the cellular biology of metals.

Detection of critical slowing down (CSD) is the dominant avenue for anticipating critical transitions from noisy time-series data. Most commonly, changes in variance and lag-1 autocorrelation [AC(1)] are used as CSD indicators. However, these indicators will only produce reliable results if the noise driving the system is white and stationary. In the more realistic case of time-correlated red noise, increasing (decreasing) the correlation of the noise will lead to spurious (masked) alarms for both variance and AC(1). Here, we propose two new methods that can discriminate true CSD from possible changes in the driving noise characteristics. We focus on estimating changes in the linear restoring rate based on Langevin-type dynamics driven by either white or red noise. We assess the capacity of our new estimators to anticipate critical transitions and show that they perform significantly better than other existing methods both for continuous-time and discrete-time models. In addition to conceptual models, we apply our methods to climate model simulations of the termination of the African Humid Period. The estimations rule out spurious signals stemming from nonstationary noise characteristics and reveal a destabilization of the African climate system as the dynamical mechanism underlying this archetype of abrupt climate change in the past.

We use symmetry analysis and density-functional theory to determine and characterize surface terminations that have a finite equilibrium magnetization density in antiferromagnetic materials. A nonzero magnetic dipole moment per unit area or “surface magnetization” can arise on particular surfaces of many antiferromagnets due to the bulk magnetic symmetries. Such surface magnetization underlies intriguing physical phenomena like interfacial magnetic coupling and can be used as a readout method of antiferromagnetic domains. However, a universal description of antiferromagnetic surface magnetization is lacking. We first introduce a classification system based on whether the surface magnetization is either sensitive or robust to roughness and on whether the magnetic dipoles at surface of interest are compensated or uncompensated when the bulk magnetic order is retained at the surface. We show that roughness-sensitive categories can be identified by a simple extension of a previously established group-theory formalism for identifying roughness-robust surface magnetization. We then map the group-theory method of identifying surface magnetization to a novel description in terms of bulk magnetic multipoles, which are already established as symmetry indicators for bulk magnetoelectric responses at both linear and higher orders. We use density-functional calculations to illustrate that nominally compensated surfaces in magnetoelectric ${\mathrm{Cr}}_2{\mathrm{O}}_3$ and centrosymmetric altermagnetic ${\mathrm{FeF}}_2$ develop a finite magnetization density at the surface, in agreement with our predictions based on both group theory and the ordering of the bulk multipoles. Our analysis provides a comprehensive basis for understanding the surface magnetic properties and their intimate correspondence to bulk magnetoelectric effects in antiferromagnets and has important implications for technologically relevant phenomena such as exchange-bias coupling.

Optimizing the energy efficiency of driving processes provides valuable insights into the underlying physics and is of crucial importance for numerous applications, from biological processes to the design of machines and robots. Knowledge of optimal driving protocols is particularly valuable at the microscale, where energy supply is often limited. Here, we experimentally and theoretically investigate the paradigmatic optimization problem of moving a potential carrying a load through a fluid, in a finite time and over a given distance, in such a way that the required work is minimized. An important step towards more realistic systems is the consideration of memory effects in the surrounding fluid, which are ubiquitous in real-world applications. Therefore, our experiments were performed in viscous and viscoelastic media, which are typical environments for synthetic and biological processes on the microscale. Despite marked differences between the protocols in both fluids, we find that the optimal control protocol and the corresponding average particle trajectory always obey a time-reversal symmetry. We show that this symmetry, which surprisingly applies here to a class of processes far from thermal equilibrium, holds universally for various systems, including active, granular, and long-range correlated media in their linear regimes. The uncovered symmetry provides a rigorous and versatile criterion for optimal control that greatly facilitates the search for energy-efficient transport strategies in a wide range of systems. Using a machine learning algorithm, we demonstrate that the algorithmic exploitation of time-reversal symmetry can significantly enhance the performance of numerical optimization algorithms.

The flux quantization is a key indication of electron pairing in superconductors. For example, the well-known $h/2e$ flux quantization is considered strong evidence for the existence of charge-$2e$, two-electron Cooper pairs. Here we report evidence for multicharge flux quantization in mesoscopic ring devices fabricated using the transition-metal kagome superconductor ${\mathrm{CsV}}_3{\mathrm{Sb}}_5$. We perform systematic magnetotransport measurements and observe unprecedented quantization of magnetic flux in units of $h/4e$ and $h/6e$ in magnetoresistance oscillations. Specifically, at low temperatures, magnetoresistance oscillations with period $h/2e$ are detected, as expected from the flux quantization for charge-$2e$ superconductivity. We find that the $h/2e$ oscillations are suppressed and replaced by resistance oscillations with $h/4e$ periodicity when the temperature is increased. Increasing the temperature further suppresses the $h/4e$ oscillations, and robust resistance oscillations with $h/6e$ periodicity emerge as evidence for charge-$6e$ flux quantization. Our observations provide the first experimental evidence for the existence of multicharge flux quanta and emergent quantum matter exhibiting higher-charge superconductivity in the strongly fluctuating region above the charge-$2e$ Cooper pair condensate, revealing new insights into the intertwined and vestigial electronic order in kagome superconductors.

Coherence time and gate fidelities in Rydberg atom quantum simulators and computers are fundamentally limited by the Rydberg state lifetime. Circular Rydberg states are highly promising candidates to overcome this limitation by orders of magnitude, as they can be effectively protected from decay due to their maximum angular momentum. We report the first realization of alkaline-earth circular Rydberg atoms trapped in optical tweezers, which provide unique and novel control possibilities due to the optically active ionic core. Specifically, we demonstrate creation of very high-$n$ ($n=79$) circular states of ${}^{88}\mathrm{Sr}$. We measure lifetimes as long as 2.55 ms at room temperature, which are achieved via cavity-assisted suppression of black-body radiation. We show coherent control of a microwave qubit encoded in circular states of nearby manifolds, and characterize the qubit coherence time via Ramsey and spin-echo spectroscopy. Finally, circular-state tweezer trapping exploiting the ${\mathrm{Sr}}^{+}$ core polarizability is quantified via measurements of the trap-induced light shift on the qubit. Our work opens routes for quantum simulations with circular Rydberg states of divalent atoms, exploiting the emergent toolbox associated with the optically active core ion.

Given a unitary evolution $U$ on a multipartite quantum system and an ensemble of initial states, how well can $U$ be simulated by local operations and classical communication (LOCC) on that ensemble? We answer this question by establishing a general, efficiently computable upper bound on the maximal LOCC simulation fidelity—what we call an “LOCC inequality.” We then apply our findings to the fundamental setting where $U$ implements a quantum Newtonian Hamiltonian over a gravitationally interacting system. Violation of our LOCC inequality can rule out the LOCCness of the underlying evolution, thereby establishing the nonclassicality of the gravitational dynamics, which can no longer be explained by a local classical field. As a prominent application of this scheme we study systems of quantum harmonic oscillators initialized in coherent states following a normal distribution and interacting via Newtonian gravity, and discuss a possible physical implementation with torsion pendula. One of our main technical contributions is the analytical calculation of the above LOCC inequality for this family of systems. As opposed to existing tests based on the detection of gravitationally mediated entanglement, our proposal works with coherent states alone, and thus it does not require the generation of largely delocalized states of motion nor the detection of entanglement, which is never created at any point in the process.

Quantum electrodynamics in $2+1$ dimensions (${\mathrm{QED}}_3$) has been proposed as a critical field theory describing the low-energy effective theory of a putative algebraic Dirac spin liquid or of quantum phase transitions in two-dimensional frustrated magnets. We provide compelling evidence that the intricate spectrum of excitations of the elementary but strongly frustrated $J_1\text{−}{J}_2$ Heisenberg model on the triangular lattice is in one-to-one correspondence to a zoo of excitations from ${\mathrm{QED}}_3$, in the quantum spin liquid regime. This evidence includes a large manifold of explicitly constructed monopole and bilinear excitations of ${\mathrm{QED}}_3$, which is thus shown to serve as an organizing principle of phases of matter in triangular lattice antiferromagnets and their low-lying excitations. Moreover, we observe signatures of emergent valence-bond solid (VBS) correlations, which can be interpreted either as evidence of critical VBS fluctuations of an emergent Dirac spin liquid or as a transition from the 120° Néel order to a VBS whose quantum critical point is described by ${\mathrm{QED}}_3$. Our results are obtained by comparing ansatz wave functions from a parton construction to exact eigenstates obtained using large-scale exact diagonalization up to $N=48$ sites.

The continued operation of the Advanced LIGO and Advanced Virgo gravitational-wave detectors is enabling the first detailed measurements of the mass, spin, and redshift distributions of the merging binary black hole population. Our present knowledge of these distributions, however, is based largely on strongly parametric models. Such models typically assume the distributions of binary parameters to be superpositions of “building block” features like power laws, peaks, dips, and breaks. Although this approach has yielded great progress in the initial characterization of the compact binary population, the strong assumptions entailed often leave it unclear which physical conclusions are driven by observation and which by the specific choice of model. In this paper, we instead model the merger rate of binary black holes as an unknown autoregressive process over the space of binary parameters, allowing us to measure the distributions of binary black hole masses, redshifts, component spins, and effective spins with near-complete agnosticism. We find the primary mass spectrum of binary black holes to be doubly peaked, with a fairly flat continuum that steepens at high masses. We identify signs of unexpected structure in the redshift distribution of binary black holes: a uniform-in-comoving volume merger rate at low redshift followed by an increase in the merger rate beyond redshift $z\approx 0.5$. Finally, we find that the distribution of black hole spin magnitudes is unimodal and concentrated at small but nonzero values, and that spin orientations span a wide range of spin-orbit misalignment angles but are also moderately unlikely to be truly isotropic.

Self-assembly is a fundamental concept in biology and of significant interest to nanotechnology. Significant progress has been made in characterizing and controlling the properties of the resulting structures, both experimentally and theoretically. However, much less is known about kinetic constraints and determinants of dynamical properties like time efficiency, although these constraints can become severe limiting factors of self-assembly processes. Here, we investigate how the time efficiency and other dynamical properties of reversible self-assembly depend on the morphology (shape) of the building blocks for systems in which the binding energy between the constituents is large. As paradigmatic examples, we stochastically simulate the self-assembly of constituents with triangular, square, and hexagonal morphology into two-dimensional structures of a specified size. We find that the constituents’ morphology critically determines the assembly time and how it scales with the size of the target structure. Our analysis reveals three key structural parameters defined by the morphology: the nucleation size and attachment order, which describe the effective order of the chemical reactions by which clusters nucleate and grow, respectively, and the growth exponent, which determines how the growth rate of an emerging structure scales with its size. Using this characterization, we formulate an effective theory of the self-assembly kinetics, which we show exhibits an inherent scale invariance. This allows us to identify general scaling laws that describe the minimal assembly time as a function of the size of the target structure. We show how these insights on the kinetics of self-assembly processes can be used to design assembly schemes that could significantly increase the time efficiency and robustness of artificial self-assembly processes.

In neural circuits, synaptic strengths influence neuronal activity by shaping network dynamics, and neuronal activity influences synaptic strengths through activity-dependent plasticity. Motivated by this fact, we study a recurrent-network model in which neuronal units and synaptic couplings are interacting dynamic variables, with couplings subject to Hebbian modification with decay around quenched random strengths. Rather than assigning a specific role to the plasticity, we use dynamical mean-field theory and other techniques to systematically characterize the neuronal-synaptic dynamics, revealing a rich phase diagram. Adding Hebbian plasticity slows activity in already chaotic networks and can induce chaos in otherwise quiescent networks. Anti-Hebbian plasticity quickens activity and produces an oscillatory component. Analysis of the Jacobian shows that Hebbian and anti-Hebbian plasticity push locally unstable modes toward the real and imaginary axes, respectively, explaining these behaviors. Both random-matrix and Lyapunov analysis show that strong Hebbian plasticity segregates network timescales into two bands, with a slow, synapse-dominated band driving the dynamics, suggesting a flipped view of the network as synapses connected by neurons. For increasing strength, Hebbian plasticity initially raises the complexity of the dynamics, measured by the maximum Lyapunov exponent and attractor dimension, but then decreases these metrics, likely due to the proliferation of stable fixed points. We compute the marginally stable spectra of such fixed points as well as their number, showing exponential growth with network size. Finally, in chaotic states with strong Hebbian plasticity, a stable fixed point of neuronal dynamics is destabilized by synaptic dynamics, allowing any neuronal state to be stored as a stable fixed point by halting the plasticity. This phase of freezable chaos offers a new mechanism for working memory.

Quantum error correction with erasure qubits promises significant advantages over standard error correction due to favorable thresholds for erasure errors. To realize this advantage in practice requires a qubit for which nearly all errors are such erasure errors, and the ability to check for erasure errors without dephasing the qubit. We demonstrate that a “dual-rail qubit” consisting of a pair of resonantly coupled transmons can form a highly coherent erasure qubit, where transmon $T_1$ errors are converted into erasure errors and residual dephasing is strongly suppressed, leading to millisecond-scale coherence within the qubit subspace. We show that single-qubit gates are limited primarily by erasure errors, with erasure probability $p_{\text{erasure}}=2.19(2)\times {10}^{-3}$ per gate while the residual errors are $\sim 40$ times lower. We further demonstrate midcircuit detection of erasure errors while introducing $<0.1\%$ dephasing error per check. Finally, we show that the suppression of transmon noise allows this dual-rail qubit to preserve high coherence over a broad tunable operating range, offering an improved capacity to avoid frequency collisions. This work establishes transmon-based dual-rail qubits as an attractive building block for hardware-efficient quantum error correction.

One of the tenets of molecular biology is that dynamic transitions between three-dimensional structures determine the function of proteins. Therefore, it seems only natural that evolutionary analysis of proteins, presently based mainly on their primary sequence, needs to shift its focus toward their function as assessed by corresponding structural transitions. This can be facilitated by recent progress in cryogenic electron microscopy that provides atomic structures of multiple conformational states for proteins and protein assemblies isolated from evolutionarily related species. In this work, we study evolutionary conservation of multiprotein assembly function by using mechanical strain as a quantitative footprint of structural transitions. We adopt the formalism of finite strain theory, developed in condensed matter physics, and apply it, as a case study, to a classical multiprotein assembly, the ATP synthase. Protein strain analysis provides a precise characterization of rotation domains that agrees with the present biophysical knowledge. In addition, we obtain a strain distribution on the protein structure associated with functional transitions. By analyzing in detail the strain patterns of the chains responsible for ATP synthesis across distinct species, we show that they are evolutionarily conserved for the same functional transition. Such conservation is not revealed by displacement or rotation patterns. Furthermore, within each functional transition, we can identify conserved strain patterns for ATP synthases isolated from different organisms. The observed strain conservation across evolutionary distant species indicates that strain should be essential in future structure-based evolutionary studies of protein function.

We synthesized polycrystalline ${\mathrm{La}}_3{\mathrm{Ni}}_2{\mathrm{O}}_{7-\delta }$ ($\delta \approx 0.07$) samples by using the sol-gel method without postannealing under high oxygen pressure, and then measured temperature-dependent resistivity under various hydrostatic pressures up to 18 GPa by using the cubic anvil and two-stage multianvil apparatus. We find that the density-wave-like anomaly in resistivity is progressively suppressed with increasing pressure and the resistivity drop corresponding to the onset of superconductivity emerges at pressure as low as $\sim 6\mathrm{GPa}$. Zero resistivity is achieved at 9 GPa below $T_{\mathrm{c}}^{\mathrm{zero}}\approx 6.6\mathrm{K}$, which increases quickly with pressure to 41 K at 18 GPa. However, the diamagnetic response was not detected in the ac magnetic susceptibility measurements up to 15 GPa, indicating a filamentary nature of the observed superconductivity in the studied pressure range. The constructed $T\text{−}P$ phase diagram reveals an intimate relationship between superconductivity, density-wave-like order, and the strange-metal-like behaviors. The observation of zero-resistance state in the polycrystalline ${\mathrm{La}}_3{\mathrm{Ni}}_2{\mathrm{O}}_{7-\delta }$ samples under high pressures not only corroborates the recent report of superconductivity in the pressurized ${\mathrm{La}}_3{\mathrm{Ni}}_2{\mathrm{O}}_7$ crystals but also facilitates further studies on this emerging family of nickelate high-$T_{\mathrm{c}}$ superconductors.

The ion-molecule reactions ${\mathrm{D}}_2^{+}+{\mathrm{NH}}_3$ and ${\mathrm{D}}_2^{+}+{\mathrm{ND}}_3$ are studied at low collision energies ($E_{\mathrm{coll}}$ from zero to approximately $k_B\times 50\mathrm{K}$), with the ${\mathrm{D}}_2^{+}$ ions in the ground rovibrational state and for different rotational temperatures of the ammonia molecules, using the Rydberg-Stark merged-beam approach. Two different rotational temperatures (approximately 15 K and approximately 40 K), measured by ($2+1$) resonance-enhanced multiphoton-ionization spectroscopy, are obtained by using a seeded supersonic expansion in He and a pure ammonia expansion, respectively. The experimental data reveal a strong enhancement of the rate coefficients at the lowest collision energies caused by the charge-dipole interaction. Calculations based on a rotationally adiabatic capture model accurately reproduce the observed kinetic-energy dependence of the rate coefficients. The rate coefficients increase with increasing rotational temperature of the ammonia molecules, which contradicts the expectation that rotational excitation should average the dipoles out. Moreover, these reactions exhibit a pronounced inverse kinetic isotope effect. The difference is caused by nuclear-spin-statistical factors and the smaller rotational constants and tunneling splittings in ${\mathrm{ND}}_3$.