5 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…
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…
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…
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…