arXiv subjects

Taegeun Song

Publications and source records attributed to Taegeun Song.

13 recordsLinked to original sources

arXiv2026

Physics-constrained machine learning for decoding multi-nanobubble configurations in graphene

Identifying multiple graphene nanobubbles from electronic spectra is challenging because their strain-induced features overlap. We develop a physics-constrained machine-learning framework that decodes nanobubble configurations from density-of-states (DOS) spectra. For spatially separated nanobubbles, previous full quantum-transport calculations established that the multi-bubble DOS is numerically equivalent to the normalized sum of the constituent single-bubble spectra. We encode this validated additive relation in a compact neural decomposition model. For each target spectrum, the basis coefficients are optimized independently, and the resulting weights directly identify the constituent geometries. The method accurately reconstructs configurations of increasing complexity and remains robust to repeated constituents, incomplete basis dictionaries, and simulated measurement noise. The framework provides an interpretable route for characterizing strain-engineered graphene nanostructures and may extend to other quantum materials with additive spectral responses.

cond-mat.mes-hall↗

arXiv2025

Asymmetric-gate Mach--Zehnder interferometry in graphene: Multi-path conductance oscillations and visibility characteristics

Graphene provides an excellent platform for investigating electron quantum interference due to its outstanding coherent properties. In the quantum Hall regime, Mach--Zehnder (MZ) electronic interferometers are realized using p--n junctions in graphene, where electron interference is highly protected against decoherence. In this work, we present a phenomenological framework for graphene-based MZ interferometry with asymmetric p--n junction configurations. We show that the enclosed interferometer area can be tuned by asymmetric gate potentials, and additional MZ pathways emerge in higher-filling-factor scenarios, e.g. $\left(\nu_{n},\nu_{p}\right)=\left(-3,+3\right)$. The resulting complicated beat oscillations in asymmetric-gate MZ interference are efficiently analyzed using a machine-learning--based Fourier transform, which yields improved peak-to-background ratios compared to conventional signal-processing techniques. Furthermore, we examine the impact of the asymmetric gate on the interference visibility, finding that interference visibility is enhanced under symmetric gate conditions.

cond-mat.mes-hall↗

arXiv2024

Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets

The application of twist engineering in van der Waals magnets has opened new frontiers in the field of two-dimensional magnetism, yielding distinctive magnetic domain structures. Despite the introduction of numerous theoretical methods, limitations persist in terms of accuracy or efficiency due to the complex nature of the magnetic Hamiltonians pertinent to these systems. In this study, we introduce a deep-learning approach to tackle these challenges. Utilizing customized, fully connected networks, we develop two deep-neural-network kernels that facilitate efficient and reliable analysis of twisted van der Waals magnets. Our regression model is adept at estimating the magnetic Hamiltonian parameters of twisted bilayer CrI3 from its magnetic domain images generated through atomistic spin simulations. The generative model excels in producing precise magnetic domain images from the provided magnetic parameters. The trained networks for these models undergo thorough validation, including statistical error analysis and assessment of robustness against noisy injections. These advancements not only extend the applicability of deep-learning methods to twisted van der Waals magnets but also streamline future investigations into these captivating yet poorly understood systems.

cond-mat.str-el↗

arXiv2024

A Local Counter-Regulatory Motif Modulates the Global Phase of Hormonal Oscillations

Counter-regulatory elements maintain dynamic equilibrium ubiquitously in living systems. The most prominent example, which is critical to mammalian survival, is that of pancreatic α and β cells producing glucagon and insulin for glucose homeostasis. These cells are not found in a single gland but are dispersed in multiple micro-organs known as the islets of Langerhans. Within an islet, these two reciprocal cell types interact with each other and with an additional cell type: the δ cell. By testing all possible motifs governing the interactions of these three cell types, we found that a unique set of positive/negative intra-islet interactions between different islet cell types functions not only to reduce the superficially wasteful zero-sum action of glucagon and insulin but also to enhance/suppress the synchronization of hormone secretions between islets under high/normal glucose conditions. This anti-symmetric interaction motif confers effective controllability for network (de)synchronization.

q-bio.TO↗

arXiv2023

Detecting Strain Effects due to Nanobubbles in Graphene Mach-Zehnder Interferometers

We investigate the effect of elastic strain on a Mach-Zehnder (MZ) interferometer created by graphene p-n junction in quantum Hall regime. We demonstrate that a Gaussian-shaped nanobubble causes detuning of the quantum Hall conductance oscillations across the p-n junction, due to the strain-induced local pseudo-magnetic fields. By performing a machine-learning-based Fourier analysis, we differentiate the nanobubble-induced Fourier component from the conductance oscillations originating from the external magnetic fields. We show that the detuning of the conductance oscillations is due to the altered pathway of quantum Hall interface channels caused by the strain-induced pseudo-magnetic fields. In the presence of the nanobubble, a new Fourier component for a magnetic flux $Φ_{0}/2$ appears, and the corresponding MZ interferometry indicates that the enclosed area is reduced by half due to the strain-mediated pathway between two quantum Hall interface channels. Our findings suggest the potential of using graphene as a strain sensor for developments in graphene-based device fabrications and measurements technologies.

cond-mat.mes-hall↗

arXiv2021

Objective comparison of methods to decode anomalous diffusion

Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers.

physics.data-an↗

arXiv2020

Analytic continuation of the self-energy via Machine Learning techniques

We develop a novel analytic continuation method for self-energies on the Matsubara domain as computed by quantum Monte Carlo simulations within dynamical mean field theory (QMC+DMFT). Unlike a maximum entropy (maxEn) procedure employed for the last thirty years, our approach is based on a machine learning (ML) technique in combination with the iterative perturbative theory impurity solver of the dynamical mean field theory self-consistent process (IPT+DMFT). The input and output training datasets for ML are simultaneously obtained from IPT+DMFT calculations on Matsubara and real frequency domains, respectively. The QMC+DMFT self-energy on real frequencies is determined from the -- usually noisy -- input QMC+DMFT self-energy on the Matsubara domain and the trained ML kernel. Our approach is free from both, bias of ML training datasets and from fitting parameters present in the maxEn method. We demonstrate the efficiency of the method on the testbed frustrated Hubbard model on the square lattice.

cond-mat.str-el↗

arXiv2020

Machine learning for the diagnosis of early stage diabetes using temporal glucose profiles

Machine learning shows remarkable success for recognizing patterns in data. Here we apply the machine learning (ML) for the diagnosis of early stage diabetes, which is known as a challenging task in medicine. Blood glucose levels are tightly regulated by two counter-regulatory hormones, insulin and glucagon, and the failure of the glucose homeostasis leads to the common metabolic disease, diabetes mellitus. It is a chronic disease that has a long latent period the complicates detection of the disease at an early stage. The vast majority of diabetics result from that diminished effectiveness of insulin action. The insulin resistance must modify the temporal profile of blood glucose. Thus we propose to use ML to detect the subtle change in the temporal pattern of glucose concentration. Time series data of blood glucose with sufficient resolution is currently unavailable, so we confirm the proposal using synthetic data of glucose profiles produced by a biophysical model that considers the glucose regulation and hormone action. Multi-layered perceptrons, convolutional neural networks, and recurrent neural networks all identified the degree of insulin resistance with high accuracy above $85\%$.

q-bio.QM↗

arXiv2020

Synchronization of active rotators interacting with environment

Multiple organs in a living system respond to environmental changes, and the signals from the organs regulate the physiological environment. Inspired by this biological feedback, we propose a simple autonomous system of active rotators to explain how multiple units are synchronized under a fluctuating environment. We find that the feedback via an environment can entrain rotators to have synchronous phases for specific conditions. This mechanism is markedly different from the simple entrainment by a common oscillatory external stimulus that is not interacting with systems. We theoretically examine how the phase synchronization depends on the interaction strength between rotators and environment. Furthermore, we successfully demonstrate the proposed model by realizing an analog electric circuit with microelectronic devices. This bio-inspired platform can be used as a sensor for monitoring varying environments, and as a controller for amplifying signals by their feedback-induced synchronization.

cond-mat.stat-mech↗

arXiv2019

Accelerated Continuous time quantum Monte Carlo method with Machine Learning

An acceleration of continuous time quantum Monte Carlo (CTQMC) methods is a potentially interesting branch of work as they are matchless as impurity solvers of a density functional theory in combination with a dynamical mean field theory approach for the description of electronic structures of strongly correlated materials. The inversion of the $k \times k$ matrix given by the diagram expansion order $k$ in the CTQMC update and the multiplication of the $k \times k$ matrix and the non-interacting Green's function to measure the impurity Green's function are computationally time-consuming. Here, we propose the CTQMC method in combination with a machine learning technique, which would eliminate the need for multiplication of the matrix with the non-interacting Green's function. This method predicts the accurate impurity Green's function and double occupancy at low temperature, and also considers the physical properties of high Matsubara frequency in a much shorter computational time than the conventional CTQMC method.

cond-mat.str-el↗

arXiv2015

Shuttle-promoted nano-mechanical current switch

We investigate electron shuttling in three-terminal nanoelectromechanocal device built on a movable metallic rod oscillating between two drains. The device shows a double-well shaped electromechanical potential tunable by a source-drain bias voltage. Four stationary regimes controllable by the bias are found for this device: (i) single stable fixed point, (ii) two stable fixed points, (iii) two limiting cycles, and (iv) single limiting cycle. In the presence of perpendicular magnetic field the Lorentz force makes possible switching from one electromechanical state to another. The mechanism of tunable transitions between various stable regimes based on the interplay between voltage controlled electromechanical instability and magnetically controlled switching is suggested. The switching phenomenon is implemented for achieving both a reliable \emph{active} current switch and sensoring of small variations of magnetic field.

cond-mat.mes-hall↗

arXiv2013

Self-sustained oscillations in nanoelectromechanical systems induced by Kondo resonance

We investigate instability and dynamical properties of nanoelectromechanical systems represented by a single-electron device containing movable quantum dot attached to a vibrating cantilever via asymmetric tunnel contact. The Kondo resonance in electron tunneling between source and shuttle facilitates self-sustained oscillations originated from strong coupling of mechanical and electronic/spin degrees of freedom. We analyze stability diagram for two-channel Kondo shuttling regime due to limitations given by the electromotive force acting on a moving shuttle and find that the saturation amplitude of oscillation is associated with the retardation effect of Kondo-cloud. The results shed light on possible ways of experimental realization of dynamical probe for the Kondo-cloud by using high tunability of mechanical dissipation as well as supersensitive detection of mechanical displacement.

cond-mat.mes-hall↗

arXiv2010

Ferromagnetically coupled magnetic impurities in a quantum point contact

We investigate the ground and excited states of interacting electrons in a quantum point contact using exact diagonalization method. We find that strongly localized states in the point contact appear when a new conductance channel opens due to momentum mismatch. These localized states form magnetic impurity states which are stable in a finite regime of chemical potential and excitation energy. Interestingly, these magnetic impurities have ferromagnetic coupling, which shed light on the experimentally observed puzzling coexistence of Kondo correlation and spin filtering in a quantum point contact.

cond-mat.mes-hall↗