arXiv subjects

Sihun Lee

Publications and source records attributed to Sihun Lee.

5 recordsLinked to original sources

arXiv2026

Reversible Superdense Ordering of Tetragonal Lithium in a Layered Material

Understanding lithium (Li) ordering and dynamics is foundational in energy storage. X-ray based experimental methods do not simultaneously provide atomic structure information together with chemical composition and local bonding information for lithium in solids. Here we employ scanning transmission electron microscopy (STEM) and combine imaging, spectroscopy, and diffraction within a single experiment, to observe, in situ, an all-solid-state electrochemical cell. By integrating multimodal STEM with other complementary techniques, we report a complete mapping of lithium intercalation in a layered system, LaTe3. We identify three ordered phases of LixLaTe3 with x ranging from 1/3 to 3 with in-plane strain of up to 5%. At a very high lithium concentration of Li3LaTe3, we discover an unexpected three-layer, superdense lithium phase with tetragonal symmetry occupying the van der Waals gap. This represents a new Li phase that is reversible. Our multimodal approach thus enables complete tracking of lithium ordering and dynamics, important for next-generation energy storage applications.

cond-mat.mtrl-sci↗

arXiv2025

Scalable and deterministic construction of moir\'e superlattice in 2D materials using stressor films

Moir\'e superlattice in two-dimensional (2D) materials provides a powerful platform to engineer emergent electronic states, yet the construction of moir\'e superlattices remains lab-scale, involving much trial and error and with little control. Here, we demonstrate the construction of a heterostrain-induced moir\'e superlattice in transition metal dichalcogenides using a scalable process that deterministically induces strain to 2D materials. By applying patterned thin-film stressors and probing the resulting structures with scanning transmission electron microscopy, we directly resolve the induced heterostrain, lattice deformations, and stacking variations that produce the moir\'e superlattice. We find that uniaxial and biaxial heterostrain give rise to distinct moir\'e patterns, including stripes and distorted hexagonal patterns. With this approach, we create in-plane polar distortions and thus in-plane polarization at the domain boundaries of the moir\'e superlattice in MoS$_2$. The deterministic and scalable construction of moir\'e patterns using a well-established scalable process opens opportunities to design new moir\'e geometries in 2D materials.

cond-mat.mtrl-sci↗

arXiv2025

Unified Cross-modal Translation of Score Images, Symbolic Music, and Performance Audio

Music exists in various modalities, such as score images, symbolic scores, MIDI, and audio. Translations between each modality are established as core tasks of music information retrieval, such as automatic music transcription (audio-to-MIDI) and optical music recognition (score image to symbolic score). However, most past work on multimodal translation trains specialized models on individual translation tasks. In this paper, we propose a unified approach, where we train a general-purpose model on many translation tasks simultaneously. Two key factors make this unified approach viable: a new large-scale dataset and the tokenization of each modality. Firstly, we propose a new dataset that consists of more than 1,300 hours of paired audio-score image data collected from YouTube videos, which is an order of magnitude larger than any existing music modal translation datasets. Secondly, our unified tokenization framework discretizes score images, audio, MIDI, and MusicXML into a sequence of tokens, enabling a single encoder-decoder Transformer to tackle multiple cross-modal translation as one coherent sequence-to-sequence task. Experimental results confirm that our unified multitask model improves upon single-task baselines in several key areas, notably reducing the symbol error rate for optical music recognition from 24.58% to a state-of-the-art 13.67%, while similarly substantial improvements are observed across the other translation tasks. Notably, our approach achieves the first successful score-image-conditioned audio generation, marking a significant breakthrough in cross-modal music generation.

cs.SD↗

arXiv2025

Boundary Regression for Leitmotif Detection in Music Audio

Leitmotifs are musical phrases that are reprised in various forms throughout a piece. Due to diverse variations and instrumentation, detecting the occurrence of leitmotifs from audio recordings is a highly challenging task. Leitmotif detection may be handled as a subcategory of audio event detection, where leitmotif activity is predicted at the frame level. However, as leitmotifs embody distinct, coherent musical structures, a more holistic approach akin to bounding box regression in visual object detection can be helpful. This method captures the entirety of a motif rather than fragmenting it into individual frames, thereby preserving its musical integrity and producing more useful predictions. We present our experimental results on tackling leitmotif detection as a boundary regression task.

cs.SD↗

arXiv2024

Six Dragons Fly Again: Reviving 15th-Century Korean Court Music with Transformers and Novel Encoding

We introduce a project that revives a piece of 15th-century Korean court music, Chihwapyeong and Chwipunghyeong, composed upon the poem Songs of the Dragon Flying to Heaven. One of the earliest examples of Jeongganbo, a Korean musical notation system, the remaining version only consists of a rudimentary melody. Our research team, commissioned by the National Gugak (Korean Traditional Music) Center, aimed to transform this old melody into a performable arrangement for a six-part ensemble. Using Jeongganbo data acquired through bespoke optical music recognition, we trained a BERT-like masked language model and an encoder-decoder transformer model. We also propose an encoding scheme that strictly follows the structure of Jeongganbo and denotes note durations as positions. The resulting machine-transformed version of Chihwapyeong and Chwipunghyeong were evaluated by experts and performed by the Court Music Orchestra of National Gugak Center. Our work demonstrates that generative models can successfully be applied to traditional music with limited training data if combined with careful design.

cs.SD↗