arXiv subjects

Junde Liu

Publications and source records attributed to Junde Liu.

10 recordsLinked to original sources

arXiv2026

Curvature-driven revival of charge density waves in non-Euclidean space

Strongly correlated quantum states, such as charge density waves (CDWs), are exquisitely sensitive to Fermi surface topology and lattice symmetry, and are typically quenched by heavy carrier doping. In two-dimensional (2D) systems, however, macroscopic geometric curvature emerges as a novel structural degree of freedom to modulate microscopic quantum coherence. This raises a compelling physical question: can non-Euclidean geometric deformations compete with extreme electronic perturbations to reshape, or even revive, a quenched macroscopic quantum order? Here, by constructing monolayer TiSe$_2$-NbSe$_2$ heterostructure on a BLG/SiC substrate for the first time, we report the curvature-driven revival of a frustrated charge order in a non-Euclidean space. Low-temperature angle-resolved photoemission spectroscopy (ARPES) reveals a massive interfacial charge transfer, which destroys the global Fermi surface nesting and completely suppresses the long-range CDW order in Euclidean flat regions. Strikingly, high-resolution scanning tunneling microscopy (STM) reveals that a novel, non-linear CDW state miraculously survives, remaining strictly localized within morphologically distorted, non-Euclidean nanoscale curved regions. Atomistic simulations unravel the structural origin of this phenomenon, demonstrating that interfacial twist and lattice mismatch spontaneously generate a corrugated superlattice.

cond-mat.mtrl-sci↗

arXiv2025

Nonvolatile photoswitching of a Mott state via reversible stacking rearrangement

Nonvolatile control of the Mott transition is a central goal in correlated-electron physics, offering access to fascinating emergent states and great potential for technological applications. Compared to chemical or mechanical approaches, ultrafast optical excitation further promises a path to create and manipulate novel non-equilibrium phases with ultimate spatiotemporal precision. However, achieving a truly nonvolatile electronic phase transition in laser-excited Mott systems remains an elusive challenge. Here, we present a highly robust and reversible method for optical control of the Mott state in van der Waals systems. Specifically, using angle-resolved photoemission spectroscopy, we observe a nonvolatile Mott-to-metallic transition in the ultrafast laser-excited charge density wave (CDW) material 1T-TaSe2. Complementary theoretical calculations reveal that this transition originates from a rearrangement of the interlayer CDW stacking. This new stacking order, formed following the ultrafast quenching of the CDW, circumvents the need for large-scale atomic sliding. Intriguingly, it introduces a significant in-plane component to the electron hopping and effectively reduces the ratio of on-site Coulomb interaction to bandwidth, thereby suppressing the Mott state and stabilizing a metallic phase. Our results establish optical-control of interlayer stacking as a versatile strategy for inducing nonvolatile phase transitions, opening a new route to tailor correlated electronic phases and realize reconfigurable high-frequency devices.

cond-mat.str-el↗

arXiv2025

The role of non-equilibrium populations in dark exciton formation

In two-dimensional transition metal dichalcogenide structures, the optical excitation of a bright exciton may be followed by the formation of a plethora of lower energy dark states. In these formation and relaxation processes between different exciton species, non-equilibrium exciton and phonon populations play a dominant role, but remain so far largely unexplored as most states are inaccessible by regular spectroscopies. Here, on the example of homobilayer 2H-MoS$_2$, we realize direct access to the full exciton relaxation cascade from experiment and theory. By measuring the energy- and in-plane momentum-resolved photoemission spectral function, we reveal a distinct fingerprint for dark excitons in a non-equilibrium excitonic occupation distribution. In excellent agreement with microscopic many-particle calculations, we quantify the timescales for the formation of a non-equilibrium dark excitonic occupation and its subsequent thermalization to 85~fs and 150~fs, respectively. Our results provide a previously inaccessible view of the complete exciton relaxation cascade, which is of paramount importance for the future characterization of non-equilibrium excitonic phases and the efficient design of optoelectronic devices based on two-dimensional materials.

cond-mat.mes-hall↗

arXiv2024

Reconciling Semantic Controllability and Diversity for Remote Sensing Image Synthesis with Hybrid Semantic Embedding

Significant advancements have been made in semantic image synthesis in remote sensing. However, existing methods still face formidable challenges in balancing semantic controllability and diversity. In this paper, we present a Hybrid Semantic Embedding Guided Generative Adversarial Network (HySEGGAN) for controllable and efficient remote sensing image synthesis. Specifically, HySEGGAN leverages hierarchical information from a single source. Motivated by feature description, we propose a hybrid semantic Embedding method, that coordinates fine-grained local semantic layouts to characterize the geometric structure of remote sensing objects without extra information. Besides, a Semantic Refinement Network (SRN) is introduced, incorporating a novel loss function to ensure fine-grained semantic feedback. The proposed approach mitigates semantic confusion and prevents geometric pattern collapse. Experimental results indicate that the method strikes an excellent balance between semantic controllability and diversity. Furthermore, HySEGGAN significantly improves the quality of synthesized images and achieves state-of-the-art performance as a data augmentation technique across multiple datasets for downstream tasks.

cs.CV↗

arXiv2024

Nonvolatile optical control of interlayer stacking order in 1T-TaS2

Nonvolatile optical manipulation of material properties on demand is a highly sought-after feature in the advancement of future optoelectronic applications. While the discovery of such metastable transition in various materials holds good promise for achieving this goal, their practical implementation is still in the nascent stage. Here, we unravel the nature of the ultrafast laser-induced hidden state in 1T-TaS2 by systematically characterizing the electronic structure evolution throughout the reversible transition cycle. We identify it as a mixed-stacking state involving two similarly low-energy interlayer orders, which is manifested as the charge density wave phase disruption. Furthermore, our comparative experiments utilizing the single-pulse writing, pulse-train erasing and pulse-pair control explicitly reveal the distinct mechanism of the bidirectional transformations -- the ultrafast formation of the hidden state is initiated by a coherent phonon which triggers a competition of interlayer stacking orders, while its recovery to the initial state is governed by the progressive domain coarsening. Our work highlights the deterministic role of the competing interlayer orders in the nonvolatile phase transition in the layered material 1T-TaS2, and promises the coherent control of the phase transition and switching speed. More importantly, these results establish all-optical engineering of stacking orders in low-dimensional materials as a viable strategy for achieving desirable nonvolatile electronic devices.

cond-mat.mtrl-sci↗

arXiv2024

Training-set-free two-stage deep learning for spectroscopic data de-noising

De-noising is a prominent step in the spectra post-processing procedure. Previous machine learning-based methods are fast but mostly based on supervised learning and require a training set that may be typically expensive in real experimental measurements. Unsupervised learning-based algorithms are slow and require many iterations to achieve convergence. Here, we bridge this gap by proposing a training-set-free two-stage deep learning method. We show that the fuzzy fixed input in previous methods can be improved by introducing an adaptive prior. Combined with more advanced optimization techniques, our approach can achieve five times acceleration compared to previous work. Theoretically, we study the landscape of a corresponding non-convex linear problem, and our results indicates that this problem has benign geometry for first-order algorithms to converge.

cond-mat.mtrl-sci↗

arXiv2023

Evidence for an Excitonic Insulator State in Ta$_2$Pd$_3$Te$_5$

The excitonic insulator (EI) is an exotic ground state of narrow-gap semiconductors and semimetals arising from spontaneous condensation of electron-hole pairs bound by attractive Coulomb interaction. Despite research on EIs dating back to half a century ago, their existence in real materials remains a subject of ongoing debate. In this study, through systematic experimental and theoretical investigations, we provide evidence for the existence of an EI ground state in a van der Waals compound Ta$_2$Pd$_3$Te$_5$. Density-functional-theory calculations suggest that it is a semimetal with a small band overlap, whereas various experiments exhibit an insulating ground state with a clear band gap. Upon incorporating electron-hole Coulomb interaction into our calculations, we obtain an EI phase where the electronic symmetry breaking opens a many-body gap. Angle-resolved photoemission spectroscopy measurements exhibit that the band gap is closed with a significant change in the dispersions as the number of thermally excited charge carriers becomes sufficiently large in both equilibrium and nonequilibrium states. Structural measurements reveal a slight breaking of crystal symmetry with exceptionally small lattice distortion in the insulating state, which cannot account for the significant gap opening. Therefore, we attribute the insulating ground state with a gap opening in Ta$_2$Pd$_3$Te$_5$ to exciton condensation, where the coupling to the symmetry-breaking electronic state induces a subtle change in the crystal structure.

cond-mat.mtrl-sci↗

arXiv2023

Time-resolved ARPES with probe energy of 6.0/7.2 eV and switchable resolution configuration

We present a detailed exposition of the design for time- and angle-resolved photoemission spectroscopy using a UV probe laser source that combines the nonlinear effects of \b{eta}-BaB2O4 and KBe2BO3F2 optical crystals. The photon energy of the probe laser can be switched between 6.0 and 7.2 eV, with the flexibility to operate each photon energy setting under two distinct resolution configurations. Under the fully optimized energy resolution configuration, we achieve an energy resolution of 8.5 meV at 6.0 eV and 10 meV at 7.2 eV. Alternatively, switching to the other configuration enhances temporal resolution, yielding a temporal resolution of 72 fs for 6.0 eV and 185 fs for 7.2 eV. We validated the performance and reliability of our system by applying it to measuring two typical materials: the topological insulator MnBi2Te4 and excitonic insulator candidate Ta2NiSe5.

cond-mat.mes-hall↗

arXiv2023

Spectroscopic data de-noising via training-set-free deep learning method

De-noising plays a crucial role in the post-processing of spectra. Machine learning-based methods show good performance in extracting intrinsic information from noisy data, but often require a high-quality training set that is typically inaccessible in real experimental measurements. Here, using spectra in angle-resolved photoemission spectroscopy (ARPES) as an example, we develop a de-noising method for extracting intrinsic spectral information without the need for a training set. This is possible as our method leverages the self-correlation information of the spectra themselves. It preserves the intrinsic energy band features and thus facilitates further analysis and processing. Moreover, since our method is not limited by specific properties of the training set compared to previous ones, it may well be extended to other fields and application scenarios where obtaining high-quality multidimensional training data is challenging.

cond-mat.mtrl-sci↗

arXiv2023

Removing grid structure in angle-resolved photoemission spectra via deep learning method

Spectroscopic data may often contain unwanted extrinsic signals. For example, in ARPES experiment, a wire mesh is typically placed in front of the CCD to block stray photo-electrons, but could cause a grid-like structure in the spectra during quick measurement mode. In the past, this structure was often removed using the mathematical Fourier filtering method by erasing the periodic structure. However, this method may lead to information loss and vacancies in the spectra because the grid structure is not strictly linearly superimposed. Here, we propose a deep learning method to effectively overcome this problem. Our method takes advantage of the self-correlation information within the spectra themselves and can greatly optimize the quality of the spectra while removing the grid structure and noise simultaneously. It has the potential to be extended to all spectroscopic measurements to eliminate other extrinsic signals and enhance the spectral quality based on the self-correlation of the spectra solely.

cond-mat.mtrl-sci↗