Publications

The use of work-function-mediated charge transfer has recently emerged as a reliable route toward nanoscale electrostatic control of individual atomic layers. Using α-RuCl3 as a 2D electron acceptor, we are able to induce emergent nano-optical behavior in hexagonal boron nitride (hBN) that arises due to interlayer charge polarization. Using scattering-type scanning near-field optical microscopy (s-SNOM), we find that a thin layer of α-RuCl3 adjacent to an hBN slab reduces the propagation length of hBN phonon polaritons (PhPs) in significant excess of what can be attributed to intrinsic optical losses. Concomitant nano-optical spectroscopy experiments reveal a novel resonance that aligns energetically with the region of excess PhP losses. These experimental observations are elucidated by first-principles density-functional theory and near-field model calculations, which show that the formation of a large interfacial dipole suppresses out-of-plane PhP propagation. Our results demonstrate the potential utility of charge-transfer heterostructures for tailoring optoelectronic properties of 2D insulators.

The continued development of novel many-body approaches to ground-state problems in physics and chemistry calls for a consistent way to assess its overall progress. Here we introduce a metric of variational accuracy, the V-score, obtained from the variational energy and its variance. We provide the most extensive curated dataset of variational calculations of many-body quantum systems to date, identifying cases where state-of-the-art numerical approaches show limited accuracy, and novel algorithms or computational platforms, such as quantum computing, could provide improved accuracy. The V-score can be used as a metric to assess the progress of quantum variational methods towards quantum advantage for ground-state problems, especially in regimes where classical verifiability is impossible.

We investigate the electronic Raman scattering of Sr

We present the mean field solution of the quantum and classical Heisenberg spin glasses, using the combination of a high precision numerical solution of the Parisi full replica symmetry breaking equations and a continuous time Quantum Monte Carlo. We characterize the spin glass order and its low-energy excitations down to zero temperature. The Heisenberg spin glass has a rougher energy landscape than its Ising analogue, and exhibits a very slow temperature evolution of its dynamical properties. We extend our analysis to the doped, metallic Heisenberg spin glass, which displays an unexpectedly slow spin dynamics reflecting the proximity to the melting quantum critical point and its associated Sachdev-Ye-Kitaev Planckian dynamics.

We visualized negative refraction of phonon polaritons, which occurs at the interface between two natural crystals. The polaritons—hybrids of infrared photons and lattice vibrations—form collimated rays that display negative refraction when passing through a planar interface between the two hyperbolic van der Waals materials: molybdenum oxide (MoO3) and isotopically pure hexagonal boron nitride (h11BN). At a special frequency ω0, these rays can circulate along closed diamond-shaped trajectories. We have shown that polariton eigenmodes display regions of both positive and negative dispersion interrupted by multiple gaps that result from polaritonic-level repulsion and strong coupling. Refraction is a familiar effect in which a light beam alters direction as it propagates from one medium to another. Negative refraction is a nonintuitive but well-established effect in which the light beam is bent in the “wrong” direction. Two groups now independently demonstrate negative refraction at the interface of two-dimensional van der Waal materials. Hu et al. used molybdenum trioxide with a graphene overlayer to show that in-plane negative refraction of mid-infrared (mid-IR) polaritons occurs at the interface and is gate tunable. Sternbach et al. used molybdenum trioxide/hexagonal boron nitride bicrystals to show that negative refraction of mid-IR polaritons occurs for propagation normal to the interface. Polaritonic negative refraction in the mid-IR provides opportunities for optical and thermal applications such as IR super-resolution imaging, nanoscale thermal manipulation, and chemical sensing devices with enhanced sensitivity. —ISO Nanoscale negative refraction is demonstrated for polaritons propagating normal to the interface of 2D bicrystals.

The study of isolated defects in solids is a natural target for classical or quantum embedding methods that treat the defect at a high level of theory and the rest of the solid at a lower level of theory. Here, in the context of active-space-based quantum embeddings, we study the performance of three active-space orbital selection schemes based on canonical (energy-ordered) orbitals, local orbitals defined in the spirit of density matrix embedding theory, and approximate natural transition orbitals. Using equation-of-motion coupled-cluster theory with single and double excitations (CCSD), we apply these active space selection schemes to the calculation of the vertical singlet excitation energy of a substitutional carbon dimer defect in hexagonal boron nitride, an oxygen vacancy in magnesium oxide, and a carbon vacancy in diamond. Especially when used in combination with a simple composite correction, we find that the best performing schemes can predict the excitation energy to about 0.1-0.2 eV of its converged value using only a few hundred orbitals, even when the full supercell has thousands of orbitals, which amounts to many-orders-of-magnitude computational savings when using correlated electronic structure theories. When compared to assigned experimental spectra and accounting for vibrational corrections, we find that CCSD predicts excitation energies that are accurate to about 0.1-0.3 eV, which is comparable to its performance in molecules and bulk solids.

Understanding the properties of well-generalizing minima is at the heart of deep learning research. On the one hand, the generalization of neural networks has been connected to the decision boundary complexity, which is hard to study in the high-dimensional input space. Conversely, the flatness of a minimum has become a controversial proxy for generalization. In this work, we provide the missing link between the two approaches and show that the Hessian top eigenvectors characterize the decision boundary learned by the neural network. Notably, the number of outliers in the Hessian spectrum is proportional to the complexity of the decision boundary. Based on this finding, we provide a new and straightforward approach to studying the complexity of a high-dimensional decision boundary; show that this connection naturally inspires a new generalization measure; and finally, we develop a novel margin estimation technique which, in combination with the generalization measure, precisely identifies minima with simple wide-margin boundaries. Overall, this analysis establishes the connection between the Hessian and the decision boundary and provides a new method to identify minima with simple wide-margin decision boundaries.

We present the mean field solution of the quantum and classical Heisenberg spin glasses, using the combination of a high precision numerical solution of the Parisi full replica symmetry breaking equations and a continuous time Quantum Monte Carlo. We characterize the spin glass order and its low-energy excitations down to zero temperature. The Heisenberg spin glass has a rougher energy landscape than its Ising analogue, and exhibits a very slow temperature evolution of its dynamical properties. We extend our analysis to the doped, metallic Heisenberg spin glass, which displays an unexpectedly slow spin dynamics reflecting the proximity to the melting quantum critical point and its associated Sachdev-Ye-Kitaev Planckian dynamics.

Multipoint vertex functions, and the four-point vertex in particular, are crucial ingredients in many-body theory. Recent years have seen significant algorithmic progress toward numerically computing their dependence on multiple frequency arguments. However, such computations remain challenging and are prone to suffer from numerical artifacts, especially in the real-frequency domain. Here, we derive estimators for multipoint vertices that are numerically more robust than those previously available. We show that the two central steps for extracting vertices from correlators, namely the subtraction of disconnected contributions and the amputation of external legs, can be achieved accurately through repeated application of equations of motion, in a manner that is symmetric with respect to all frequency arguments and involves only fully renormalized objects. The symmetric estimators express the core part of the vertex and all asymptotic contributions through separate expressions that can be computed independently, without subtracting the large-frequency limits of various terms with different asymptotic behaviors. Our strategy is general and applies equally to the Matsubara formalism, the real-frequency zero-temperature formalism, and the Keldysh formalism. We demonstrate the advantages of the symmetric improved estimators by computing the Keldysh four-point vertex of the single-impurity Anderson model using the numerical renormalization group.

We investigate the possible superconducting instabilities of strongly correlated electron materials using a generalization of linear response theory to external pairing fields depending on frequency. We compute a pairing susceptibility depending on two times, allowing us to capture dynamical pairing and in particular odd-frequency solutions. We first benchmark this method on the attractive one-band Hubbard model and then consider the superconductivity of strontium ruthenate Sr