arXiv subjects

Wenjiang Zhou

Publications and source records attributed to Wenjiang Zhou.

16 recordsLinked to original sources

arXiv2026

Intrinsically low thermal conductivity of stoichiometric lithium niobate:Experimental measurement and microscopic origin

With the rapid development of integrated electro-optic and nonlinear optical devices based on lithium niobate (LiNbO$_3$, LN), thermal management is becoming a critical area of focus. However, experimental measurement of thermal transport in stoichiometric LiNbO$_3$ (sLN) remains scarce, and the intrinsic microscopic mechanisms remain to be established. Here, we combine the laser pump-probe technique of frequency-domain thermoreflectance (FDTR) with state-of-the-art machine-learned atomistic simulations to comprehensively investigate thermal transport in sLN. The measured and simulated room-temperature thermal conductivity ($\kappa$) values of sLN agree well, which are orders-of-magnitude lower than that of many classic and emerging semiconductors such as silicon. Furthermore, the temperature-dependent $\kappa$ exhibits a $T^{-\alpha}$ scaling with $\alpha$ near unity, suggesting that thermal transport is dominated by intrinsic phonon-phonon scattering. By comparing sLN with cubic boron arsenide (cBAs) which serves as an ultrahigh-$\kappa$ benchmark, we reveal that harmonic properties are not responsible for the low $\kappa$ of sLN, which feature phonon heat capacity and group velocities that are either higher than or comparable to those in cBAs. Instead, the low $\kappa$ originates from substantially stronger anharmonicity and larger scattering phase space. These two factors collectively suppress phonon lifetimes by 1-2 orders of magnitude, leading to a maximum phonon mean free path of approximately 140 nm. As a result, notable size effects emerge in thin-film sLN below 1 $\mu$m, with $\kappa$ dropping to half the bulk value at 10 nm. Altogether, our findings establish a fundamental understanding of thermal transport in sLN and provide atomistic insights for thermal management in advanced lithium niobate technologies.

cond-mat.mtrl-sci↗

arXiv2026

Zero-Field Thermal Hall Effect in Insulator

Fourier's law dictates that heat flow is usually parallel to the applied temperature gradient. However, under a high magnetic field, heat flow carried by both electrons in conductors and phonons in insulators can be deflected, a phenomenon known as thermal Hall effect. Intriguingly, we observe at zero field a spontaneous thermal Hall effect in an antiferromagnetic insulator. Despite a vanishingly small uncompensated magnetization, the magnitude of this effect is surprisingly large, comparable to typical responses induced by several teslas of external field. This zero-field behavior indicates that charge-neutral heat carriers can be governed by an intrinsic effective field arising from the unique spin arrangement. Our discovery challenges the centuries-old preconception of heat conduction and open up new avenues for exploring non-trivial topological responses in quantum materials.

cond-mat.str-el↗

arXiv2026

VITA-QinYu: Expressive Spoken Language Model for Role-Playing and Singing

Human speech conveys expressiveness beyond linguistic content, including personality, mood, or performance elements, such as a comforting tone or humming a song, which we formalize as role-playing and singing. We present VITA-QinYu, the first expressive end-to-end (E2E) spoken language model (SLM) that goes beyond natural conversation to support both role-playing and singing generation. VITA-QinYu adopts a hybrid speech-text paradigm that extends interleaved text-audio modeling with multi-codebook audio tokens, a design enabling richer paralinguistic representation while preserving a clear separation between modalities to avoid interference. We further develop a comprehensive data generation pipeline to synthesize a total of 15.8K hours of natural conversation, role-playing, and singing data for training. VITA-QinYu demonstrates superior expressiveness, outperforming peer SLMs by 7 percentage points on objective role-playing benchmarks, and surpassing peer models by 0.13 points on a 5-point MOS scale for singing. Simultaneously, it achieves state-of-the-art conversational accuracy and fluency, exceeding prior SLMs by 1.38 and 4.98 percentage points on the C3 and URO benchmarks, respectively. We open-source our code and models and provide an easy-to-use demo with full-stack support for streaming and full-duplex interaction.

cs.CL↗

arXiv2026

LaDA-Band: Language Diffusion Models for Vocal-to-Accompaniment Generation

Vocal-to-accompaniment (V2A) generation, which aims to transform a raw vocal recording into a fully arranged accompaniment, inherently requires jointly addressing an accompaniment trilemma: preserving acoustic authenticity, maintaining global coherence with the vocal track, and producing dynamic orchestration across a full song. Existing open-source approaches typically make compromises among these goals. Continuous-latent generation models can capture long musical spans but often struggle to preserve fine-grained acoustic detail. In contrast, discrete autoregressive models retain local fidelity but suffer from unidirectional generation and error accumulation in extended contexts. We present LaDA-Band, an end-to-end framework that introduces Discrete Masked Diffusion to the V2A task. Our approach formulates V2A generation as Discrete Masked Diffusion, i.e., a global, non-autoregressive denoising formulation that combines the representational advantages of discrete audio codec tokens with full-sequence bidirectional context modeling. This design improves long-range structural consistency and temporal synchronization while preserving crisp acoustic details. Built on this formulation, LaDA-Band further introduces a dual-track prefix-conditioning architecture, an auxiliary replaced-token detection objective for weakly anchored accompaniment regions, and a two-stage progressive curriculum to scale Discrete Masked Diffusion to full-song vocal-to-accompaniment generation. Extensive experiments on both academic and real-world benchmarks show that LaDA-Band consistently improves acoustic authenticity, global coherence, and dynamic orchestration over existing baselines, while maintaining strong performance even without auxiliary reference audio. Codes and audio samples are available at https://github.com/Duoluoluos/TME-LaDA-Band .

cs.SD↗

arXiv2026

TQCodec: Towards neural audio codec for high-fidelity music streaming

We propose TQCodec, a neural audio codec designed for high-bitrate, high-fidelity music streaming. Unlike existing neural codecs that primarily target ultra-low bitrates (<= 16kbps), TQCodec operates at 44.1 kHz and supports bitrates from 32 kbps to 128 kbps, aligning with the standard quality of modern music streaming platforms. The model adopts an encoder-decoder architecture based on SEANet for efficient on-device computation and introduces several enhancements: an imbalanced network design for improved quality with low overhead, SimVQ for mid-frequency detail preservation, and a phase-aware waveform loss. Additionally, we introduce a perception-driven band-wise bit allocation strategy to prioritize perceptually critical lower frequencies. Evaluations on diverse music datasets demonstrate that TQCodec achieves superior audio quality at target bitrates, making it well-suited for high-quality audio applications.

cs.SD↗

arXiv2025

Heat transport in superionic materials via machine-learned molecular dynamics

Precise modeling and understanding of heat transport in the superionic phase are of great interest. Although simulations combining Green-Kubo (GK) molecular dynamics with machine-learned potentials (MLPs) stand as a promising approach, substantial challenges remain due to the crucial impact of atomic diffusion. Here, we first show that the thermal conductivity (${\kappa}$) of superionic materials calculated via conventional GK integral of the energy flux varies notably with the MLP model. Subsequently, we highlight that reliable, model-independent $\kappa$ values can be obtained by applying Onsager's reciprocal relations to correctly capture the coupled heat and mass transport. Remarkably, an anomalously invariant $\kappa$ can be observed over a wide temperature range, distinct from the characteristic trends in traditional crystals and glasses. In addition, we illustrate that conventional $\kappa$ decompositions into kinetic, potential, and cross terms suffer from ambiguities in the physical interpretation, despite their mathematical rigor. Finally, we propose a criterion for the necessity of the Onsager correction and reveal the underlying mechanism as a competition between thermally and chemically driven ion fluxes.

cond-mat.mtrl-sci↗

arXiv2025

Deep Dubbing: End-to-End Auto-Audiobook System with Text-to-Timbre and Context-Aware Instruct-TTS

The pipeline for multi-participant audiobook production primarily consists of three stages: script analysis, character voice timbre selection, and speech synthesis. Among these, script analysis can be automated with high accuracy using NLP models, whereas character voice timbre selection still relies on manual effort. Speech synthesis uses either manual dubbing or text-to-speech (TTS). While TTS boosts efficiency, it struggles with emotional expression, intonation control, and contextual scene adaptation. To address these challenges, we propose DeepDubbing, an end-to-end automated system for multi-participant audiobook production. The system comprises two main components: a Text-to-Timbre (TTT) model and a Context-Aware Instruct-TTS (CA-Instruct-TTS) model. The TTT model generates role-specific timbre embeddings conditioned on text descriptions. The CA-Instruct-TTS model synthesizes expressive speech by analyzing contextual dialogue and incorporating fine-grained emotional instructions. This system enables the automated generation of multi-participant audiobooks with both timbre-matched character voices and emotionally expressive narration, offering a novel solution for audiobook production.

eess.AS↗

arXiv2025

MuseTalk: Real-Time High-Fidelity Video Dubbing via Spatio-Temporal Sampling

Real-time video dubbing that preserves identity consistency while achieving accurate lip synchronization remains a critical challenge. Existing approaches face a trilemma: diffusion-based methods achieve high visual fidelity but suffer from prohibitive computational costs, while GAN-based solutions sacrifice lip-sync accuracy or dental details for real-time performance. We present MuseTalk, a novel two-stage training framework that resolves this trade-off through latent space optimization and spatio-temporal data sampling strategy. Our key innovations include: (1) During the Facial Abstract Pretraining stage, we propose Informative Frame Sampling to temporally align reference-source pose pairs, eliminating redundant feature interference while preserving identity cues. (2) In the Lip-Sync Adversarial Finetuning stage, we employ Dynamic Margin Sampling to spatially select the most suitable lip-movement-promoting regions, balancing audio-visual synchronization and dental clarity. (3) MuseTalk establishes an effective audio-visual feature fusion framework in the latent space, delivering 30 FPS output at 256*256 resolution on an NVIDIA V100 GPU. Extensive experiments demonstrate that MuseTalk outperforms state-of-the-art methods in visual fidelity while achieving comparable lip-sync accuracy. %The codes and models will be made publicly available upon acceptance. The code is made available at \href{https://github.com/TMElyralab/MuseTalk}{https://github.com/TMElyralab/MuseTalk}

cs.CV↗

arXiv2025

Ultrahigh interfacial thermal conductance for cooling gallium oxide electronics using cubic boron arsenide

Gallium oxide (Ga$_2$O$_3$) has attracted significant interest for its unique potential especially in power electronics. However, its low and anisotropic thermal conductivity poses a major challenge for heat dissipation. Here, we explore an effective cooling strategy centering on the heterogeneous integration of $β$-Ga$_2$O$_3$ devices with cubic boron arsenide (cBAs), an emerging material with an ultrahigh thermal conductivity $κ$ of ~1300 Wm$^{-1}$K$^{-1}$. Machine-learned potentials for representative $β$-Ga$_2$O$_3$/cBAs interfaces are trained, enabling accurate and efficient calculation of the interfacial thermal conductance $G$ via nonequilibrium molecular dynamics. At 300 K, remarkable $G$ values of 749$\pm$33 MWm$^{-2}$K$^{-1}$ and 824$\pm$35 MWm$^{-2}$K$^{-1}$ are predicted for Ga-As and O-B bonding across the interface, respectively, which are primarily attributed to the well-matched phonon density of states considering the similar Debye temperatures of $β$-Ga$_2$O$_3$ and cBAs. Moreover, finite-element simulations directly show a notable device temperature reduction when comparing cBAs with other substrates. The simultaneously ultrahigh $κ$ and $G$ highlight cBAs as an ideal substrate for Ga$_2$O$_3$ electronics.

cond-mat.mtrl-sci↗

arXiv2024

Insight into the effect of force error on the thermal conductivity from machine-learned potentials

Machine-learned potentials (MLPs) have been extensively used to obtain the lattice thermal conductivity via atomistic simulations. However, the impact of force errors in various MLPs on thermal transport has not been widely recognized and remains to be fully understood. Here, we employ MLP-driven molecular dynamics (MD) and anharmonic lattice dynamics (LD) to systematically investigate how the calculated thermal conductivity varies with the force errors, using boron arsenide as a prototypical material. We consistently observe an underestimation of thermal conductivity in MD simulations with three different MLPs including the neuroevolution potential, deep potential, and moment tensor potential. We provide a robust extrapolation scheme based on controlled force noises via the Langevin thermostat to correct this underestimation. The corrected results achieve a good agreement with previous experimental measurement from 200 K to 600 K. In contrast, the thermal conductivity values from LD calculations with MLPs readily align with the experimental data, which is attributed to the much smaller effects of the force errors on the force-constant calculations.

cond-mat.mtrl-sci↗

arXiv2024

Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials

Path-integral molecular dynamics (PIMD) simulations are crucial for accurately capturing nuclear quantum effects in materials. However, their computational intensity and reliance on multiple software packages often limit their applicability at large scales. Here, we present an integration of PIMD methods, including thermostatted ring-polymer molecular dynamics (TRPMD), into the open-source GPUMD package, combined with highly accurate and efficient machine-learned neuroevolution potential (NEP) models. This approach achieves almost the accuracy of first-principles calculations with the computational efficiency of empirical potentials, enabling large-scale atomistic simulations that incorporate nuclear quantum effects. We demonstrate the efficacy of the combined NEP-PIMD approach by examining various thermal properties of diverse materials, including lithium hydride (LiH), three porous metal-organic frameworks (MOFs), liquid water, and elemental aluminum. For LiH, our NEP-PIMD simulations successfully capture the isotope effect, reproducing the experimentally observed dependence of the lattice parameter on the reduced mass. For MOFs, our results reveal that achieving good agreement with experimental data requires consideration of both nuclear quantum effects and dispersive interactions. For water, our PIMD simulations capture the significant impact of nuclear quantum effects on its microscopic structure. For aluminum, the TRPMD method effectively captures thermal expansion and phonon properties, aligning well with quantum mechanical predictions. This efficient NEP-PIMD approach opens new avenues for exploring complex material properties influenced by nuclear quantum effects, with potential applications across a broad range of materials.

cond-mat.mtrl-sci↗

arXiv2024

Phonon heat conduction across slippery interfaces in twisted graphite

Interlayer rotation in van der Waals (vdW) materials offers great potential for manipulating phonon dynamics and heat flow in advanced electronics with ever higher compactness and power density. However, despite extensive theoretical efforts in recent years, experimental measurements remain scarce especially due to the critical challenges of preparing single-crystalline twisted interfaces and probing interfacial thermal transport with sufficient resolution. Here, we exploited the intrinsic twisted interfaces in highly oriented pyrolytic graphite (HOPG). By developing novel experimental schemes based on microfabricated mesas, we managed to achieve simultaneous mechanical characterizations and thermal measurements. In particular, we pushed the HOPG mesas with a microprobe to identify and rotate single-crystalline intrinsic interfaces owing to their slippery nature as is well known in structural superlubricity. Remarkably, we observed over 30-fold suppression of thermal conductance for the slippery interfaces by using epitaxial graphite as a control. Nonetheless, the interfacial conductance remains around 600 $\mathrm{MWm^{-2}K^{-1}}$ which surpasses the highest values for artificially stacked vdW structures by more than five times. Further, atomic simulations revealed the predominant role of the transverse acoustic phonons. Together, our findings highlight a general physical picture that directly correlates interfacial thermal transport with sliding resistance, and lay the foundation for twist-enabled thermal management which are particularly beneficial to twistronics and slidetronics.

cond-mat.mes-hall↗

arXiv2024

Correcting force error-induced underestimation of lattice thermal conductivity in machine learning molecular dynamics

Machine learned potentials (MLPs) have been widely employed in molecular dynamics (MD) simulations to study thermal transport. However, literature results indicate that MLPs generally underestimate the lattice thermal conductivity (LTC) of typical solids. Here, we quantitatively analyze this underestimation in the context of the neuroevolution potential (NEP), which is a representative MLP that balances efficiency and accuracy. Taking crystalline silicon, GaAs, graphene, and PbTe as examples, we reveal that the fitting errors in the machine-learned forces against the reference ones are responsible for the underestimated LTC as they constitute external perturbations to the interatomic forces. Since the force errors of a NEP model and the random forces in the Langevin thermostat both follow a Gaussian distribution, we propose an approach to correcting the LTC by intentionally introducing different levels of force noises via the Langevin thermostat and then extrapolating to the limit of zero force error. Excellent agreement with experiments is obtained by using this correction for all the prototypical materials over a wide range of temperatures. Based on spectral analyses, we find that the LTC underestimation mainly arises from increased phonon scatterings in the low-frequency region caused by the random force errors.

cond-mat.mtrl-sci↗

arXiv2024

Molecular dynamics simulations of heat transport using machine-learned potentials: A mini review and tutorial on GPUMD with neuroevolution potentials

Molecular dynamics (MD) simulations play an important role in understanding and engineering heat transport properties of complex materials. An essential requirement for reliably predicting heat transport properties is the use of accurate and efficient interatomic potentials. Recently, machine-learned potentials (MLPs) have shown great promise in providing the required accuracy for a broad range of materials. In this mini review and tutorial, we delve into the fundamentals of heat transport, explore pertinent MD simulation methods, and survey the applications of MLPs in MD simulations of heat transport. Furthermore, we provide a step-by-step tutorial on developing MLPs for highly efficient and predictive heat transport simulations, utilizing the neuroevolution potentials (NEPs) as implemented in the GPUMD package. Our aim with this mini review and tutorial is to empower researchers with valuable insights into cutting-edge methodologies that can significantly enhance the accuracy and efficiency of MD simulations for heat transport studies.

cond-mat.mtrl-sci↗

arXiv2024

Isotope engineering of carrier mobility via Fröhlich electron-phonon interaction

Isotope effects on phonon properties and transport have been predicted and observed for decades. However, despite the crucial impact of electron-phonon interactions, the effect of isotopes on electron transport remains largely unexplored. Here, by using first-principles calculations, we theoretically predict that the electron mobility of lithium hydride (LiH) can increase by up to ~100% as $^3\rm{H}$ is replaced with $^1\rm{H}$. This remarkable phenomenon is primarily attributed to the isotope engineering of the Fröhlich interaction by the mass-induced line shift of the longitudinal optical (LO) phonons. Notably, the isotope-dependent absorption of LO phonons dominates while the isotope-insensitive emission process is mostly suppressed due to energy conservation. We further propose general guidelines for evaluating isotope effects on carrier transport in different materials.

cond-mat.mtrl-sci↗

arXiv2023

MBTFNet: Multi-Band Temporal-Frequency Neural Network For Singing Voice Enhancement

A typical neural speech enhancement (SE) approach mainly handles speech and noise mixtures, which is not optimal for singing voice enhancement scenarios. Music source separation (MSS) models treat vocals and various accompaniment components equally, which may reduce performance compared to the model that only considers vocal enhancement. In this paper, we propose a novel multi-band temporal-frequency neural network (MBTFNet) for singing voice enhancement, which particularly removes background music, noise and even backing vocals from singing recordings. MBTFNet combines inter and intra-band modeling for better processing of full-band signals. Dual-path modeling are introduced to expand the receptive field of the model. We propose an implicit personalized enhancement (IPE) stage based on signal-to-noise ratio (SNR) estimation, which further improves the performance of MBTFNet. Experiments show that our proposed model significantly outperforms several state-of-the-art SE and MSS models.

cs.SD↗