Publications

Van der Waals (vdW) heterostructures host many-body quantum phenomena that can be tuned in situ using electrostatic gates. These gates are often microstructured graphite flakes that naturally form plasmonic cavities, confining light in discrete standing waves of current density due to their finite size. Their resonances typically lie in the GHz - THz range, corresponding to the same μeV - meV energy scale characteristic of many quantum effects in the materials they electrically control. This raises the possibility that built-in cavity modes could be relevant for shaping the low-energy physics of vdW heterostructures. However, capturing this light-matter interaction remains elusive as devices are significantly smaller than the diffraction limit at these wavelengths, hindering far-field spectroscopic tools. Here, we report on the sub-wavelength cavity electrodynamics of graphene embedded in a vdW heterostructure plasmonic microcavity. Using on-chip THz spectroscopy, we observed spectral weight transfer and an avoided crossing between the graphite cavity and graphene plasmon modes as the graphene carrier density was tuned, revealing their ultrastrong coupling. Our findings show that intrinsic cavity modes of metallic gates can sense and manipulate the low-energy electrodynamics of vdW heterostructures. This opens a pathway for deeper understanding of emergent phases in these materials and new functionality through cavity control.

Machine learning (ML) is a promising tool for the detection of phases of matter. However, ML models are also known for their black-box construction, which hinders understanding of what they learn from the data and makes their application to novel data risky. Moreover, the central challenge of ML is to ensure its good generalization abilities, i.e., good performance on data outside the training set. Here, we show how the informed use of an interpretability method called class activation mapping (CAM), and the analysis of the latent representation of the data with the principal component analysis (PCA) can increase trust in predictions of a neural network (NN) trained to classify quantum phases. In particular, we show that we can ensure better out-of-distribution generalization in the complex classification problem by choosing such an NN that, in the simplified version of the problem, learns a known characteristic of the phase. We show this on an example of the topological Su-Schrieffer-Heeger (SSH) model with and without disorder, which turned out to be surprisingly challenging for NNs trained in a supervised way. This work is an example of how the systematic use of interpretability methods can improve the performance of NNs in scientific problems.

We perform extensive simulations of the two-dimensional cavity-coupled electron gas in a modulating potential as a minimal model for cavity quantum materials. These simulations are enabled by a newly developed quantum-electrodynamical (QED) auxiliary-field quantum Monte Carlo method. We present a procedure to greatly reduce finite-size effects in such calculations. Based on our results, we show that a modified version of weak-coupling perturbation theory is remarkably accurate for a large parameter region. We further provide a simple parameterization of the light-matter correlation energy as a functional of the cavity parameters and the electronic density. These results provide a numerical foundation for the development of the QED density functional theory, which was previously reliant on analytical approximations, to allow quantitative modeling of a wide range of systems with light-matter coupling.

The spin-fermion (SF) model postulates that the dominant coupling between low-energy fermions in near critical metals is mediated by collective spin fluctuations (paramagnons) peaked at the Néel wave vector, Q

We propose a novel algorithm for calculating the ground-state energy of quantum many-body systems by combining auxiliary-field quantum Monte Carlo (AFQMC) with tensor-train sketching. In AFQMC, having a good trial wavefunction to guide the random walk is crucial for avoiding sign problems. Typically, this trial wavefunction is fixed throughout the simulation. Our proposed method iterates between determining a new trial wavefunction in the form of a tensor train, derived from the current walkers, and using this updated trial wavefunction to anchor the next phase of AFQMC. Numerical results demonstrate that our algorithm is highly accurate for large spin systems, achieving a relative error of (10

Characterizing complex many-body phases of matter has been a central question in quantum physics for decades. Numerical methods built around approximations of the renormalization group (RG) flow equations have offered reliable and systematically improvable answers to the initial question -- what simple physics drives quantum order and disorder? The flow equations are a very high dimensional set of coupled nonlinear equations whose solution is the two particle vertex function, a function of three continuous momenta that describes particle-particle scattering and encodes much of the low energy physics including whether the system exhibits various forms of long ranged order. In this work, we take a simple and interpretable data-driven approach to the open question of compressing the two-particle vertex. We use PCA and an autoencoder neural network to derive compact, low-dimensional representations of underlying physics for the case of interacting fermions on a lattice. We quantify errors in the representations by multiple metrics and show that a simple linear PCA offers more physical insight and better out-of-distribution (zero-shot) generalization than the nominally more expressive nonlinear models. Even with a modest number of principal components (10 - 20), we find excellent reconstruction of vertex functions across the phase diagram. This result suggests that many other many-body functions may be similarly compressible, potentially allowing for efficient computation of observables. Finally, we identify principal component subspaces that are shared between known phases, offering new physical insight.

Carlo is a Monte Carlo simulation framework written in Julia. It provides MPI-parallel scheduling, organized storage of input, checkpoint, and output files, as well as statistical postprocessing. With a minimalist design, it aims to aid the development of high-quality Monte Carlo codes, especially for demanding applications in condensed matter and statistical physics. This hands-on user guide shows how to implement a simple code with Carlo and provides benchmarks to show its efficacy.

Graphene is a privileged 2D platform for hosting confined light-matter excitations known as surface plasmon-polaritons (SPPs), as it possesses low intrinsic losses with a high degree of optical confinement. However, the inherently isotropic optical properties of graphene limit its ability to guide and focus SPPs, making it less suitable than anisotropic elliptical and hyperbolic materials as a platform for polaritonic lensing and canalization. Here, we present the graphene/CrSBr heterostructure as an engineered 2D interface that hosts highly anisotropic SPP propagation over a wide range of frequencies in the mid-infrared and terahertz. Using a combination of scanning tunneling microscopy (STM), scattering-type scanning near-field optical microscopy (s-SNOM), and first-principles calculations, we demonstrate mutual doping in excess of 1013 cm−2 holes/electrons between the interfacial layers of graphene/CrSBr heterostructures. SPPs in graphene activated by charge transfer interact with charge-induced anisotropic intra- and interband transitions in the interfacial doped CrSBr, leading to preferential SPP propagation along the quasi-1D chains that compose each CrSBr layer. This multifaceted proximity effect both creates SPPs and endows them with anisotropic transport and propagation lengths that differ by an order-of-magnitude between the two in-plane crystallographic axes of CrSBr.

Coherent manipulation of magnetism through the lattice provides unprecedented opportunities for controlling spintronic functionalities on the ultrafast timescale. Such nonthermal control conventionally involves nonlinear excitation of Raman-active phonons which are coupled to the magnetic order. Linear excitation, in contrast, holds potential for more efficient and selective modulation of magnetic properties. However, the linear channel remains uncharted, since it is conventionally considered forbidden in inversion symmetric quantum materials. Here, we harness strong coupling between magnons and Raman-active phonons to achieve both linear and quadratic excitation regimes of magnon-polarons, magnon-phonon hybrid quasiparticles. We demonstrate this by driving magnon-polarons with an intense terahertz pulse in the van der Waals antiferromagnet FePS3. Such excitation behavior enables a unique way to coherently control the amplitude of magnon-polaron oscillations by tuning the terahertz field strength and its polarization. The polarimetry of the resulting coherent oscillation amplitude breaks the crystallographic C2 symmetry due to strong interference between different excitation channels. Our findings unlock a wide range of possibilities to manipulate material properties, including modulation of exchange interactions by phonon-Floquet engineering.

For molecules and solids, we developed efficient MPI-parallel algorithms for evaluating the second-order exchange term with bare, statically screened, and dynamically screened interactions. We employ the resulting term in a fully self-consistent manner together with scGW, resulting in the following vertex-corrected scGW schemes: scGWSOX, scGWSOSEX, scGW2SOSEX, and scG3W2theories. We show that for the vertex evaluation, the reduction of scaling by tensor hypercontraction (THC) has two limiting execution regimes. We used the resulting code to perform the largest (by the number of orbitals) fully self-consistent calculations with the SOX term. We demonstrate that our procedure allows for a reliable evaluation of even small energy differences. Utilizing a broken-symmetry approach, we explore the influence of the SOX term on the effective magnetic exchange couplings. We show that the treatment of SOX has a significant impact on the obtained values of the effective exchange constants, which we explain through a self-energy dependence on an effective dielectric constant. We confirm this explanation by analyzing natural orbitals and local changes in charge transfer quantifying superexchange. Our analysis explains the structure of weak electron correlation responsible for the modulation of superexchange in both molecules and solids. Finally, for solids, we evaluate Neel temperatures utilizing the high-temperature expansion and compare the results obtained with experimental measurements.