arXiv subjects

Yi Sheng Ng

Publications and source records attributed to Yi Sheng Ng.

4 recordsLinked to original sources

arXiv2026

Emergent surface resonance from charge density wave symmetry breaking in TiSe2

Surface confined electronic states provide a fertile ground for discovering emergent phenomena that have no counterpart in the bulk, offering new routes to manipulate correlations, symmetry breaking, and dimensionality at the atomic scale. Here, we show that charge density wave (CDW) symmetry breaking can yield a surface states in 1T-TiSe2. Micro angle resolved photoemission spectroscopy resolves a sharp, two dimensional surface resonant state (SRS) that emerges within the CDW reconstructed low energy spectrum. The SRS exhibits notable temperature dependence and its spectral weight collapses around 160 K, while CDW transition temperature TCDW is commonly reported as 202 K. Slab DFT+U calculations reproduce a surface localized resonance when CDW folding brings valence and conduction states into near degeneracy, suggesting a correlation tuned, surface selective origin. These results point to a form of correlation-tuned surface resonance in a layered CDW compound and suggest a framework for engineering low dimensional quantum states in van der Waals materials via symmetry breaking and electronic structure tuning.

cond-mat.mtrl-sci↗

arXiv2026

Electronic Coherence Evolution at the Nearly Commensurate Incommensurate CDW Boundary of 1T-TaS2

Transition metal dichalcogenides host a variety of charge density wave phases that couple lattice, charge, and correlation effects. In 1T-TaS2, the commensurate and nearly commensurate states are well characterized, yet the transition near 350 K into the incommensurate phase has lacked direct momentum resolved insight. Here we use temperature dependent angle resolved photoemission spectroscopy to track the electronic structure across this transition. We observe a suppression of quasiparticle spectral weight at the Brillouin zone center, coincident with the transport anomaly, but without clear evidence of a full band gap opening. The transition appears to involve momentum dependent redistribution of spectral weight, consistent with a loss of coherence that reshapes the Fermi surface while leaving conduction dispersions largely intact. These results suggest that the nearly commensurate incommensurate transition may not align with a conventional metal insulator transition picture, but rather as an electronic reconstruction driven by loss of coherence. Our work provides new microscopic insight into the resistivity anomaly near room temperature and may guide design principles for collective electronic switching in Transition metal dichalcogenides.

cond-mat.str-el↗

arXiv2025

Intercalation induced quasi-freestanding layer in TiSe$_2$

Angle-resolved photoemission spectroscopy is employed to study the electronic structure of bulk TiSe2 before and after doping with potassium impurities. A splitting in the conduction band into two branches is observed after room-temperature deposition. The splitting energy increases to approximately 130 meV when the sample is cooled to 40 K. One branch exhibits a non-dispersive two-dimensional feature, while other one shows the characteristics of three dimensional bulk band dispersion. Core level spectroscopy suggests that the K impurities predominantly occupy the intercalated sites within the van derWaals gap. The results indicate the formation of a quasi-freestandingTiSe2 layer. Additionally, doping completely suppresses the periodic lattice distortion in the surface region. These findings are further supported by density functional theory calculations, which compare the band structure of monolayer and bulk TiSe2 with experimental data. Thus, the dimensional and intrinsic electronic properties of 1T-TiSe2 can be controlled through the intercalation procedure used in this work.

cond-mat.mtrl-sci↗

arXiv2023

Advances of Machine Learning in Materials Science: Ideas and Techniques

In this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large database and repositories appearing everywhere. Traditionally, materials science is a trial-and-error field, in both the computational and experimental departments. With the advent of machine learning-based techniques, there has been a paradigm shift: materials can now be screened quickly using ML models and even generated based on materials with similar properties; ML has also quietly infiltrated many sub-disciplinary under materials science. However, ML remains relatively new to the field and is expanding its wing quickly. There are a plethora of readily-available big data architectures and abundance of ML models and software; The call to integrate all these elements in a comprehensive research procedure is becoming an important direction of material science research. In this review, we attempt to provide an introduction and reference of ML to materials scientists, covering as much as possible the commonly used methods and applications, and discussing the future possibilities.

cond-mat.mtrl-sci↗