16 citations · 64 across the 13 of their papers we have counts for
13 papers
Physics-constrained machine learning for decoding multi-nanobubble configurations in graphene
Jihye Kim, Taegeun Song, Nojoon Myoung
Identifying multiple graphene nanobubbles from electronic spectra is challenging because their strain-induced features overlap. We develop a physics-constrained machine-learning fr…
Asymmetric-gate Mach--Zehnder interferometry in graphene: Multi-path conductance oscillations and visibility characteristics
Taegeun Song, Nojoon Myoung
Graphene provides an excellent platform for investigating electron quantum interference due to its outstanding coherent properties. In the quantum Hall regime, Mach--Zehnder (MZ) e…
Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets
Woo Seok Lee, Taegeun Song, Kyoung-Min Kim
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. D…
Detecting Strain Effects due to Nanobubbles in Graphene Mach-Zehnder Interferometers
Nojoon Myoung, Taegeun Song, Hee Chul Park
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 nan…
Objective comparison of methods to decode anomalous diffusion
Gorka Muñoz-Gil, Giovanni Volpe, Miguel Angel Garcia-March +31
Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion fr…
Analytic continuation of the self-energy via Machine Learning techniques
Taegeun Song, Roser Valenti, Hunpyo Lee
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+DMF…
Machine learning for the diagnosis of early stage diabetes using temporal glucose profiles
Woo Seok Lee, Junghyo Jo, Taegeun Song
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 c…
Synchronization of active rotators interacting with environment
Taegeun Song, Heetae Kim, Seung-Woo Son +1
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, w…
Accelerated Continuous time quantum Monte Carlo method with Machine Learning
Taegeun Song, Hunpyo Lee
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…
A Local Counter-Regulatory Motif Modulates the Global Phase of Hormonal Oscillations
Dong-Ho Park, Taegeun Song, Danh-Tai Hoang +2
Counter-regulatory elements maintain dynamic equilibrium ubiquitously in living systems. The most prominent example, which is critical to mammalian survival, is that of pancreatic…
Shuttle-promoted nano-mechanical current switch
Taegeun Song, Leonid Y. Gorelik, Robert I. Shekhter +2
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 shap…
Self-sustained oscillations in nanoelectromechanical systems induced by Kondo resonance
Taegeun Song, Mikhail N. Kiselev, Konstantin Kikoin +2
We investigate instability and dynamical properties of nanoelectromechanical systems represented by a single-electron device containing movable quantum dot attached to a vibrating…
Ferromagnetically coupled magnetic impurities in a quantum point contact
Taegeun Song, Kang-Hun Ahn
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 p…