Search

By combining ab initio downfolding with cluster dynamical mean-field theory, we study the degree of correlations in the low-temperature structures of the breathing-mode kagome van der Waals materials Nb$_3$(F,Cl,Br,I)$_8$. We show that the Coulomb correlation strength steadily increases from I to Br, Cl, and F, allowing us to identify Nb$_3$I$_8$ as a weakly correlated (obstructed atomic) insulator whose gap is only mildly affected by the local Coulomb interaction. Nb$_3$Br$_8$ and Nb$_3$Cl$_8$ are strongly correlated insulators, whose gaps are significantly influenced by Coulomb-induced vertex corrections. Nb$_3$F$_8$ is a prototypical bulk Mott-insulator whose gap is initially opened by strong correlation effects. Angle-resolved photoemission spectroscopy measurements comparing Nb$_3$Br$_8$ and Nb$_3$I$_8$ allow us to experimentally confirm these findings by revealing spectroscopic footprints of the degree of correlation. Our calculations further predict that the entire material family can be tuned into correlated charge-transfer or Mott-insulating phases upon electron or hole doping. Our magnetic property analysis additionally shows that inter-layer magnetic frustrations in the high-temperature phase drive the lattice phase transition to the low-temperature structures. The accompanying bilayer hybridization through inter-layer dimerization yields magnetic singlet ground states in the Cl, Br, and I compounds. Our findings establish Nb$_3$X$_8$ as a robust, versatile, and tunable class for van der Waals-based Coulomb and Mott engineering and allow us to speculate on the symmetry-breaking effects necessary for the recently observed Josephson diode effect in NbSe$_2$/Nb$_3$Br$_8$/NbSe$_2$ heterostructures., Comment: 23 pages, 18 figures

This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed approach builds upon two existing methodologies for reduced and full-order non-intrusive modeling, namely Operator Inference (OpInf) and sparse Full-Order Model (sFOM) inference. We decompose the domain into two complementary subdomains which exhibit fast and slow singular value decay. The dynamics of the subdomain exhibiting slow singular value decay are learned with sFOM while the dynamics with intrinsically low dimensionality on the complementary subdomain are learned with OpInf. The resulting, coupled OpInf-sFOM formulation leverages the computational efficiency of OpInf and the high resolution of sFOM, and thus enables fast non-intrusive predictions for conditions beyond those sampled in the training data set. A novel regularization technique with a closed-form solution based on the Gershgorin disk theorem is introduced to promote stable sFOM and OpInf models. We also provide a data-driven indicator for the subdomain selection and ensure solution smoothness over the interface via a post-processing interpolation step. We evaluate the efficiency of the approach in terms of offline and online speedup through a quantitative, parametric computational cost analysis. We demonstrate the coupled OpInf-sFOM formulation for two test cases: a one-dimensional Burgers' model for which accurate predictions beyond the span of the training snapshots are presented, and a two-dimensional parametric model for the Pine Island Glacier ice thickness dynamics, for which the OpInf-sFOM model achieves an average prediction error on the order of $1 \%$ with an online speedup factor of approximately $8\times$ compared to the numerical simulation.

Ice sheet simulations suffer from vast parametric uncertainties, such as the basal sliding boundary condition or geothermal heat flux. Quantifying the resulting uncertainties in predictions is of utmost importance to support judicious decision-making, but high-fidelity simulations are too expensive to embed within uncertainty quantification (UQ) computations. UQ methods typically employ Monte Carlo simulation to estimate statistics of interest, which requires hundreds (or more) of ice sheet simulations. Cheaper low-fidelity models are readily available (e.g., approximated physics, coarser meshes), but replacing the high-fidelity model with a lower fidelity surrogate introduces bias, which means that UQ results generated with a low-fidelity model cannot be rigorously trusted. Multifidelity UQ retains the high-fidelity model but expands the estimator to shift computations to low-fidelity models, while still guaranteeing an unbiased estimate. Through this exploitation of multiple models, multifidelity estimators guarantee a target accuracy at reduced computational cost. This paper presents a comprehensive multifidelity UQ framework for ice sheet simulations. We present three multifidelity UQ approaches -- Multifidelity Monte Carlo, Multilevel Monte Carlo, and the Best Linear Unbiased Estimator -- that enable tractable UQ for continental-scale ice sheet simulations. We demonstrate the techniques on a model of the Greenland ice sheet to estimate the 2015-2050 ice mass loss, verify their estimates through comparison with Monte Carlo simulations, and give a comparative performance analysis. For a target accuracy equivalent to 1 mm sea level rise contribution at 95% confidence, the multifidelity estimators achieve computational speedups of two orders of magnitude.

Scanning tunneling microscopy is the method of choice for characterizing charge density waves by imaging the variation in atomic-scale contrast of the surface. Due to the measurement principle of scanning tunneling microscopy, the electronic and lattice degrees of freedom are convoluted, making it difficult to disentangle a structural displacement from spatial variations in the electronic structure. In this work, we characterize the influence of the displacement of the surface-terminating Se atoms on the 3 x 3 charge density wave contrast in scanning probe microscopy images of 2H-NbSe$_2$. In scanning tunneling microscopy images, we observe the 3 x 3 charge density wave superstructure and atomic lattice at all probed tip-sample distances. In contrast, non-contact atomic force microscopy images show both periodicities only at small tip-sample distances while, unexpectedly, a 3 x 3 superstructure is present at larger tip-sample distances. Using density functional theory calculations, we qualitatively reproduce the experimental findings and indicate that the 3 x 3 superstructure at different tip-sample distances in non-contact atomic force microscopy images is a result from different underlying interactions. In addition, we show that the displacement of the surface-terminating Se atoms has a negligible influence to the contrast in scanning tunneling microscopy images. Our combined experimental and theoretic work presents a method on how to discriminate the influence of the surface corrugation from the variation of the charge density to the charge density wave contrast in scanning probe microscopy images, which can provide insights into the influence of structural disorder to a charge density wave ground state.

By determining the superconducting lower and upper critical fields $H_\mathrm{c1}(T)$ and $H_\mathrm{c2}(T)$, respectively, in a high-purity spherical Sr$_2$RuO$_4$ sample via ac-susceptibility measurements, we obtain the temperature dependence of the coherence length $\xi$ and the penetration depth $\lambda$ down to 0.04$T_c$. Given the high sample quality, the observed $T^2$ dependence of $\lambda$ at low temperatures cannot be explained in terms of impurity effects. Instead, we argue that the weak type-II superconductor Sr$_2$RuO$_4$ has to be treated in the non-local limit. In that limit, the penetration depth data agree with a gap structure having vertical line nodes, while horizontal line nodes cannot account for the observation.

We construct and analyze families of periodic delay orbits for a class of delay differential equations in two dimensions depending on two real-valued functions. These families are parametrized by the delay parameter. It is possible to represent the dependency of these periodic delay orbits on the delay parameter by a curve in the plane, without loss of information. It turns out that the singularities of these curves necessarily are cusps in the non-degenerate case. After discussing degenerate situations in general, we explain how to glue different families of periodic delay orbits at degeneracies in the delay parameter., Comment: 21 pages, 11 figures

We consider optimal sensor placement for a family of linear Bayesian inverse problems characterized by a deterministic hyper-parameter. The hyper-parameter describes distinct configurations in which measurements can be taken of the observed physical system. To optimally reduce the uncertainty in the system's model with a single set of sensors, the initial sensor placement needs to account for the non-linear state changes of all admissible configurations. We address this requirement through an observability coefficient which links the posteriors' uncertainties directly to the choice of sensors. We propose a greedy sensor selection algorithm to iteratively improve the observability coefficient for all configurations through orthogonal matching pursuit. The algorithm allows explicitly correlated noise models even for large sets of candidate sensors, and remains computationally efficient for high-dimensional forward models through model order reduction. We demonstrate our approach on a large-scale geophysical model of the Perth Basin, and provide numerical studies regarding optimality and scalability with regard to classic optimal experimental design utility functions.

High $T_c$ cuprate strange metals are noted for a DC-resistivity that scales linearly with $T$ from the onset of superconductivity to the crystal melting temperature, indicative of a Planckian dissipation life time $\tau_{\hbar}\simeq \hbar /(k_B T)$. At the same time, the optical conductivity ceases to be of Drude form at high temperatures, suggesting a change of the underlying dynamics that surprisingly leaves the $T$-linear DC-resistivity unaffected. We use the AdS/CFT correspondence that describes strongly coupled, densely entangled metals to study DC thermo-electrical transport and the optical conductivities of the local quantum critical Gubser-Rocha holographic strange metal in the presence of a lattice potential, a prime candidate to compare with experiment. We find that the DC-resistivity is linear in $T$ at low temperatures for a range of lattice strengths and wavevectors, even as it transitions between different dissipative regimes. At weak lattice potential the optical conductivity evolves with increasing temperature from a Drude form to a bad-metal characterized by a mid-IR resonance without changing the DC transport, similar to that seen in cuprate strange metals. This mid-IR peak and its temperature evolution can be understood as a consequence of Umklapp hydrodynamics: hydrodynamic perturbations are Bloch modes in the presence of a lattice. At strong lattice potential an incoherent metal is realized instead where momentum conservation no longer plays a role in transport. In this regime the thermal diffusivity can be explained by Planckian dissipation originating in universal microscopic chaos, similar to holographic metals with strong homogeneous momentum relaxation. The charge diffusivity does not submit to this chaos explanation, even though the continuing linear-in-$T$ DC resistivity saturates to an apparent universal slope, numerically equal to a Planckian rate., Comment: 64 pages, 16 figures; v2: minor changes, some rewriting of sections VI and VII

We consider optimal sensor placement for hyper-parameterized linear Bayesian inverse problems, where the hyper-parameter characterizes nonlinear flexibilities in the forward model, and is considered for a range of possible values. This model variability needs to be taken into account for the experimental design to guarantee that the Bayesian inverse solution is uniformly informative. In this work we link the numerical stability of the maximum a posterior point and A-optimal experimental design to an observability coefficient that directly describes the influence of the chosen sensors. We propose an algorithm that iteratively chooses the sensor locations to improve this coefficient and thereby decrease the eigenvalues of the posterior covariance matrix. This algorithm exploits the structure of the solution manifold in the hyper-parameter domain via a reduced basis surrogate solution for computational efficiency. We illustrate our results with a steady-state thermal conduction problem.