6 citations · 10 across the 4 of their papers we have counts for
8 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…
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…