arXiv subjects

Yungi Jeong

Publications and source records attributed to Yungi Jeong.

6 recordsLinked to original sources

arXiv2026

Superconductivity near quarter and half filling of a strongly correlated triangular Hubbard band in twisted trilayer WSe2

The triangular-lattice Hubbard model is one of the paradigmatic settings for studying correlated electronic systems with geometric frustration that can give rise to a variety of phases. Recently, twisted bilayer TMD systems have emerged as attractive experimental platforms for realising such a model in the moderate-correlation regime. Alternating twisted trilayer TMD systems are expected to realise stronger correlations than bilayer systems owing to their mirror symmetry. Here we report the discovery of superconductivity near quarter- and half-filling, together with various correlated phases, in alternating twisted trilayer WSe2 (TTWSe2). At half filling, a robust correlated insulating state persists over a broad range of displacement fields, signalling the strongly correlated regime. Upon doping, superconductivity emerges over an extended region of the phase diagram. Near quarter filling, another superconducting state appears flanked by correlated metals. Our results establish the TTWSe2 system as a compelling platform for studying strongly correlated triangular-lattice Hubbard physics.

cond-mat.supr-con↗

arXiv2026

Breaking the Stochasticity Barrier: An Adaptive Variance-Reduced Method for Variational Inequalities

Stochastic non-convex non-concave optimization, formally characterized as Stochastic Variational Inequalities (SVIs), presents unique challenges due to rotational dynamics and the absence of a global merit function. While adaptive step-size methods (like Armijo line-search) have revolutionized convex minimization, their application to this setting is hindered by the Stochasticity Barrier: the noise in gradient estimation masks the true operator curvature, triggering erroneously large steps that destabilize convergence. In this work, we propose VR-SDA-A (Variance-Reduced Stochastic Descent-Ascent with Armijo), a novel algorithm that integrates recursive momentum (STORM) with a rigorous Same-Batch Curvature Verification mechanism. We introduce a theoretical framework based on a Lyapunov potential tracking the Operator Norm, proving that VR- SDA-A achieves an oracle complexity of O(epsilon -3) for finding an epsilon-stationary point in general Lipschitz continuous operators. This matches the optimal rate for non-convex minimization while uniquely enabling automated step-size adaptation in the saddle-point setting. We validate our approach on canonical rotational benchmarks and non-convex robust regression tasks, demonstrating that our method effectively suppresses limit cycles and accelerates convergence with reduced dependence on manual learning rate scheduling.

math.OC↗

arXiv2025

Evidence for electron localisation in a moiré-of-moiré superlattice

The localisation of electrons in a lattice potential is an quantum-mechanical phenomenon and is often associated with remarkable physical properties of solids involving electron spins, electric polarisations and topological effects. In particular, even a small amount of distortion of the lattice potential can localise otherwise-delocalised quantum states in low-dimensional electron systems, dramatically influencing their thermodynamic properties and charge-transport behaviour. Study of such electron localisation induced by an aperiodic lattice potential remains exceptionally challenging in solid-state systems, since extrinsic disorders can trivially trap electrons in potential minima near disorders, obscuring the underlying quantum-mechanical origin of localisation phenomena. Van der Waals heterostructures can provide an alternative route for explorations of the phenomena via the emergence of superlattice potentials generated by rotating and stacking individual layers. Here, we report strong signatures of electron localisation in helical trilayer graphene, where the interplay of two moiré patterns gives rise to a moiré-of-moiré superlattice with distinct regions of moiré-periodic and moiré-aperiodic potentials. Remarkably, our measurements reveal the presence of double moiré-induced bands and high-order Brown-Zak oscillations, which are direct reflections of the periodic region with two constituent moiré patterns, and a superimposed anomalous hysteretic signal attributable to the aperiodic region. The data strongly suggest that electron wave functions are partially localised driven by the loss of a periodic lattice potential. Our work provides insight into the effects of spatially inhomogeneous lattice potentials on the low-dimensional electronic states and introduces a promising approach to control electron localisation for practical applications in solid-state devices.

cond-mat.mes-hall↗

arXiv2024

Interplay of valley, layer and band topology towards interacting quantum phases in moiré bilayer graphene

In Bernal-stacked bilayer graphene (BBG), the Landau levels give rise to an intimate connection between valley and layer degrees of freedom. Adding a moiré superlattice potential enriches the BBG physics with the formation of topological minibands - potentially leading to tunable exotic quantum transport. Here, we present magnetotransport measurements of a high-quality bilayer graphene-hexagonal boron nitride (hBN) heterostructure. The zero-degree alignment generates a strong moiré superlattice potential for the electrons in BBG and the resulting Landau fan diagram of longitudinal and Hall resistance displays a Hofstadter butterfly pattern with a high level of detail. We demonstrate that the intricate relationship between valley and layer degrees of freedom controls the topology of moiré-induced bands, significantly influencing the energetics of interacting quantum phases in the BBG superlattice. We further observe signatures of field-induced correlated insulators, helical edge states and clear quantizations of interaction-driven topological quantum phases, such as symmetry broken Chern insulators.

cond-mat.mes-hall↗

arXiv2024

Characterization of a graphene-hBN superlattice field effect transistor

Graphene provides a unique platform for hosting high quality 2D electron systems. Encapsulating graphene with hexagonal boron nitride (hBN) to shield it from noisy environments offers the potential to achieve ultrahigh performance nanodevices, such as photodiodes and transistors. However, the absence of a bandgap at the Dirac point presents challenges for using this system as a useful transistor. In this study, we investigated the functionality of hBN-aligned monolayer graphene as a field effect transistor (FET). By precisely aligning the hBN and graphene, bandgaps open at the first Dirac point and at the hole-doped induced Dirac point via an interfacial moiré potential. To characterize this as a submicrometer scale FET, we fabricated a global bottom gate to tune the density of a conducting channel and a local top gate to switch off this channel. This demonstrated that the system could be tuned to an optimal on/off ratio regime by separately controlling the gates. These findings provide a valuable reference point for the further development of FETs based on graphene heterostructures.

cond-mat.mes-hall↗

arXiv2023

AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Scheme

Mechanical defects in real situations affect observation values and cause abnormalities in multivariate time series, such as sensor values or network data. To perceive abnormalities in such data, it is crucial to understand the temporal context and interrelation between variables simultaneously. The anomaly detection task for time series, especially for unlabeled data, has been a challenging problem, and we address it by applying a suitable data degradation scheme to self-supervised model training. We define four types of synthetic outliers and propose the degradation scheme in which a portion of input data is replaced with one of the synthetic outliers. Inspired by the self-attention mechanism, we design a Transformer-based architecture to recognize the temporal context and detect unnatural sequences with high efficiency. Our model converts multivariate data points into temporal representations with relative position bias and yields anomaly scores from these representations. Our method, AnomalyBERT, shows a great capability of detecting anomalies contained in complex time series and surpasses previous state-of-the-art methods on five real-world benchmarks. Our code is available at https://github.com/Jhryu30/AnomalyBERT.

cs.LG↗