arXiv subjects

Sung Yun Lee

Publications and source records attributed to Sung Yun Lee.

4 recordsLinked to original sources

arXiv2025

Frustrated phonon with charge density wave in vanadium Kagome metal

The formation of a star of David CDW superstructure, resulting from the coordinated displacements of vanadium ions on a corner sharing triangular lattice, has garnered significant attention to comprehend the influence of electron phonon interaction within geometrically intricate lattice of Kagome metals, specifically AV3Sb5 (where A represents K, Rb, or Cs). However, understanding of the underlying mechanism behind CDW formation, coupled with symmetry protected lattice vibrations, remains elusive. Here, from femtosecond time resolved X ray scattering experiments, we reveal that the phonon mode, associated with Cs ions out-of-plane motion, becomes frustrated in the CDW phase. Furthermore, we observed the photoinduced emergence of a metastable CDW phase, facilitated by alleviating the frustration. By not only elucidating the longstanding puzzle surrounding the intervention of phonons but introducing the phononic frustration, this research offers fresh insights into the competition between phonons and periodic lattice distortions, a phenomenon widespread in other correlated quantum materials including layered high TC superconductors.

cond-mat.str-el↗

arXiv2024

Hidden dormant phase mediating the glass transition in disordered matter

Metallic glass is a frozen liquid with structural disorder that retains degenerate free energy without spontaneous symmetry breaking to become a solid. For over half a century, this puzzling structure has raised fundamental questions about how structural disorder impacts glass-liquid phase transition kinetics, which remain elusive without direct evidence. In this study, through single-pulse, time-resolved imaging using X-ray free-electron lasers, we visualized the glass-to-liquid transition, revealing a previously hidden dormant phase that does not involve any macroscopic volume change within the crossover regime between the two phases. Although macroscopically inactive, nanoscale redistribution occurs, forming channeld low-density bands within this dormant phase that drives the glass transition. By providing direct microscopic evidence, this work presents a new perspective on the phase transition process in disordered materials, which can be extended to various liquid and solid phases in other complex systems.

cond-mat.dis-nn↗

arXiv2024

Photoinduced surface plasmon control of ultrafast melting modes in Au nanorods

Photoinduced ultrafast phenomena in materials exhibiting nonequilibrium behavior can lead to the emergence of exotic phases beyond the limits of thermodynamics, presenting opportunities for femtosecond photoexcitation. Despite extensive research, the ability to actively control quantum materials remains elusive owing to the lack of clear evidence demonstrating the explicit control of phase-changing kinetics through light-matter interactions. To address this drawback, we leveraged single-pulse time-resolved X-ray imaging of Au nanorods undergoing photoinduced melting to showcase control over the solid-to-liquid transition process through the use of localized surface plasmons. Our study uncovers transverse or longitudinal melting processes accompanied by characteristic oscillatory distortions at different laser intensities. Numerical simulations confirm that the localized surface plasmons, excited by polarized laser fields, dictate the melting modes through anharmonic lattice deformations. These results provide direct evidence of photoinduced surface plasmon-mediated ultrafast control of matter, establishing a foundation for the customization of material kinetics using femtosecond laser fields.

cond-mat.mes-hall↗

arXiv2024

Deep-learning real-time phase retrieval of imperfect diffraction patterns from X-ray free-electron lasers

Machine learning is attracting surging interest across nearly all scientific areas by enabling the analysis of large datasets and the extraction of scientific information from incomplete data. Data-driven science is rapidly growing, especially in X-ray methodologies, where advanced light sources and detection technologies accumulate vast amounts of data that exceed meticulous human inspection capabilities. Despite the increasing demands, the full application of machine learning has been hindered by the need for data-specific optimizations. In this study, we introduce a new deep-learning-based phase retrieval method for imperfect diffraction data. This method provides robust phase retrieval for simulated data and performs well on weak-signal single-pulse diffraction data from X-ray free-electron lasers. Moreover, the method significantly reduces data processing time, facilitating real-time image reconstructions that are crucial for high-repetition-rate data acquisition. Thus, this approach offers a reliable solution to the phase problem and is expected to be widely adopted across various research areas.

physics.app-ph↗