arXiv subjects

Mingda Li

Publications and source records attributed to Mingda Li.

At least 37 records · Page 2Linked to original sources

arXiv2026

Magnetic Field-Enhanced Graphene Superconductivity with Record Pauli-Limit Violation

Spin-polarized superconductors offer a rare platform for studying electronic correlations, but few candidate systems have been experimentally confirmed to date. Here, we report the observation of a spin-polarized superconducting state, denoted SC5, in WSe2-proximitized rhombohedral trilayer graphene. At in-plane magnetic field B|| = 0 T, SC5 has a critical temperature of 68 mK and an out-of-plane critical magnetic field of only 12 mT. Surprisingly, these values are significantly enhanced as B|| increases, and the superconductivity persists to B|| = 8.8 T. This value corresponds to a record-high Pauli-limit violation ratio of at least 80 among all superconductors, while the true critical field is beyond the limit of our instrument. We conclude that SC5 experiences a canting crossover from Ising-type to spin-polarized superconductor with increased B||.

cond-mat.supr-con↗

arXiv2025

The Loss Landscape of Powder X-Ray Diffraction-Based Structure Optimization Is Too Rough for Gradient Descent

Solving crystal structures from powder X-ray diffraction (XRD) is a central challenge in materials characterization. In this work, we study the powder XRD-to-structure mapping using gradient descent optimization, with the goal of recovering the correct structure from moderately distorted initial states based solely on XRD similarity. We show that commonly used XRD similarity metrics result in a highly non-convex landscape, complicating direct optimization. Constraining the optimization to the ground-truth crystal family significantly improves recovery, yielding higher match rates and increased mutual information and correlation scores between structural similarity and XRD similarity. Nevertheless, the landscape may remain non-convex along certain symmetry axes. These findings suggest that symmetry-aware inductive biases could play a meaningful role in helping learning models navigate the inverse mapping from diffraction to structure.

cond-mat.mtrl-sci↗

arXiv2025

Chimeric states of matter: Meissner effect without superconductivity

Symmetry is central to how we classify phases of matter: solids break spatial translations, superfluids break particle-number conservation, and superconductors "break" gauge symmetry. Mixed anomalies involving higher-form symmetries, however, present a generalization of spontaneous symmetry breaking that admits a wider and more versatile set of possibilities. We introduce chimeric states of matter, in which aspects of broken and unbroken phases coexist. We find that the Meissner effect -- usually regarded as the defining hallmark of superconductivity -- can occur in media that are resistive or even insulating when probed by electric fields. We demonstrate this by constructing an effective field theory of "symmetry chimerization" and propose that Josephson junction networks could provide a laboratory realization. These results broaden the landscape of possible phases of matter, showing that physical media can mix features of symmetry-restored and symmetry-broken states in a single substrate.

cond-mat.supr-con↗

arXiv2025

Reinforcement learning-guided optimization of critical current in high-temperature superconductors

High-temperature superconductors are essential for next-generation energy and quantum technologies, yet their performance is often limited by the critical current density ($J_c$), which is strongly influenced by microstructural defects. Optimizing $J_c$ through defect engineering is challenging due to the complex interplay of defect type, density, and spatial correlation. Here we present an integrated workflow that combines reinforcement learning (RL) with time-dependent Ginzburg-Landau (TDGL) simulations to autonomously identify optimal defect configurations that maximize $J_c$. In our framework, TDGL simulations generate current-voltage characteristics to evaluate $J_c$, which serves as the reward signal that guides the RL agent to iteratively refine defect configurations. We find that the agent discovers optimal defect densities and correlations in two-dimensional thin-film geometries, enhancing vortex pinning and $J_c$ relative to the pristine thin-film, approaching 60\% of theoretical depairing limit with up to 15-fold enhancement compared to random initialization. This RL-driven approach provides a scalable strategy for defect engineering, with broad implications for advancing HTS applications in fusion magnets, particle accelerators, and other high-field technologies.

cond-mat.mtrl-sci↗

arXiv2025

ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models

Uncertainty Quantification (UQ) is a promising approach to improve model reliability, yet quantifying the uncertainty of Large Language Models (LLMs) is non-trivial. In this work, we establish a connection between the uncertainty of LLMs and their invariance under semantic-preserving intervention from a causal perspective. Building on this foundation, we propose a novel grey-box uncertainty quantification method that measures the variation in model outputs before and after the semantic-preserving intervention. Through theoretical justification, we show that our method provides an effective estimate of epistemic uncertainty. Our extensive experiments, conducted across various LLMs and a variety of question-answering (QA) datasets, demonstrate that our method excels not only in terms of effectiveness but also in computational efficiency.

cs.CL↗

arXiv2025

Strain effect on optical properties and quantum weight of monolayer MnBi$_2$X$_4$ (X = Te, Se, S)

Manipulating the optical and quantum properties of two-dimensional (2D) materials through strain engineering is not only fundamentally interesting but also provides significant benefits across various applications. In this work, we employ first-principles calculations to investigate the effects of strain on the magnetic and optical properties of the monolayer MnBi$_2$X$_4$ (X = Te, Se, S). Our results indicate that biaxial strain enhances the Mn magnetic moment, while uniaxial strains reduce it. Significantly, the strain-dependent behavior, quantified through the quantum weight, can be leveraged to control the system's quantum geometry and topological features. Particularly, uniaxial strains reduce the quantum weight and introduce anisotropy, thus providing an additional degree of freedom to tailor device functionalities. Finally, by analyzing chemical bonds under various strain directions, we elucidate how the intrinsic ductile or brittle fracture behavior of MnBi$_2$X$_4$ could impact fabrication protocols and structural stability. These insights pave the way for strain-based approaches to optimize the quantum properties in 2D magnetic topological insulators in practical device contexts.

cond-mat.mtrl-sci↗

arXiv2025

High-sensitivity molecular spectroscopy of SrOH using magneto-optical trapping

Polyatomic molecules are projected to be powerful tools in searches for physics beyond the Standard Model (BSM), including new CP-violating (CPV) interactions and ultralight dark matter (UDM) particles. Certain degrees of freedom present in polyatomic molecules enhance the sensitivity of these searches, as well as reject systematic errors, but necessitate extensive high-precision spectroscopy to identify pathways for optical cycling and quantum state readout. Here we show how a magneto-optical trap (MOT) can be used to locate weak optical transitions and identify rovibronic states for optical cycling and quantum control. We demonstrate this spectroscopic approach with strontium monohydroxide (SrOH), which is a candidate for both CPV and UDM searches. We identify two new repumping transitions in SrOH and implement them in a deeper optical cycle to achieve 32400(4700) trapped molecules, a 4.5-fold increase over the previous, shallower cycle. In addition, we determine the energy spacing between the $X^{2}Σ^{+}(200)$ and $X^{2}Σ^{+}(03^10)$ vibrational manifolds of SrOH, confirming the existence of numerous low-frequency rovibrational transitions that are sensitive to temporal variations of the proton-to-electron mass ratio, a predicted effect of the existence of UDM.

physics.atom-ph↗

arXiv2025

Family of Unconventional Superconductivities in Crystalline Graphene

Unconventional superconductors exhibit multiple broken symmetries and exceed the range of the Bardeen-Cooper-Schrieffer (BCS) theory. For instance, time-reversal symmetry can be broken in addition to the gauge symmetry, resulting in superconductors that can be enhanced or induced by a magnetic field. However, such unconventional superconductivities are more vulnerable to impurities than their BCS counterparts, requiring highly ordered and clean material systems to observe them. Crystalline rhombohedral multilayer graphene is a promising platform to explore unconventional superconductivity due to its superior material quality and gate-tunable strong correlation effects. Here we report transport measurements of rhombohedral tetralayer and pentalayer graphene, where a spectrum of superconductivities in a clean limit are observed. Three of them (SC2-4) show highly unusual enhancements by magnetic fields: 1. SC2 is strengthened by an in-plane field; 2. SC3 is boosted by a small out-of-plane field; 3. SC4 is induced by an in-plane field. All these superconductors are robust against an in-plane field up to 8.5 Tesla, exceeding the Pauli limit of conventional superconductors by tens of times and suggesting their unconventional nature. Moreover, we observed that proximitized spin-orbit coupling generates a plethora of new superconductors in the phase diagram, while maintaining the high quality of bare rhombohedral graphene. Our work establishes a family of new superconductors in rhombohedral multilayer graphene, which also provides an ideal platform to engineer non-Abelian quasiparticles by proximitizing with quantum anomalous Hall states existing in the same material system.

cond-mat.mes-hall↗

arXiv2025

Optical Trapping of SrOH Molecules for Dark Matter and T-violation Searches

We report an optical dipole trap of strontium monohydroxide (SrOH) with 1400(300) trapped molecules. Through optical pumping, we access vibrational states that are proposed for improved probes of the electron's electric dipole moment (eEDM) and ultralight dark matter (UDM). For each of these states, the lifetime of trapped molecules is measured, and found to be consistent with spontaneous radiative decay and black-body excitation limits, making this platform viable for these eEDM and UDM searches.

physics.atom-ph↗

arXiv2025

Enhancing Materials Discovery with Valence Constrained Design in Generative Modeling

Diffusion-based deep generative models have emerged as powerful tools for inverse materials design. Yet, many existing approaches overlook essential chemical constraints such as oxidation state balance, which can lead to chemically invalid structures. Here we introduce CrysVCD (Crystal generator with Valence-Constrained Design), a modular framework that integrates chemical rules directly into the generative process. CrysVCD first employs a transformer-based elemental language model to generate valence-balanced compositions, followed by a diffusion model to generate crystal structures. The valence constraint enables orders-of-magnitude more efficient chemical valence checking, compared to pure data-driven approaches with post-screening. When fine-tuned on stability metrics, CrysVCD achieves 85% thermodynamic stability and 68% phonon stability. Moreover, CrysVCD supports conditional generation of functional materials, enabling discovery of candidates such as high thermal conductivity semiconductors and high-$κ$ dielectric compounds. Designed as a general-purpose plugin, CrysVCD can be integrated into diverse generative pipeline to promote chemical validity, offering a reliable, scientifically grounded path for materials discovery.

cond-mat.mtrl-sci↗

arXiv2025

Tuning chiral anomaly signature in a Dirac semimetal via fast-ion implantation

Cd$_3$As$_2$ is a prototypical Dirac semimetal that hosts a chiral anomaly and thereby functions as a platform to test high-energy physics hypotheses and to realize energy efficient applications. Here we use a combination of accelerator-based fast ion implantation and theory-driven planning to enhance the negative longitudinal magnetoresistance (NLMR)--a signature of a chiral anomaly--in Nb-doped Cd$_3$As$_2$ thin films. High-energy ion implantation is commonly used to investigate semiconductors and nuclear materials but is rarely employed to study quantum materials. We use electrical transport and transmission electron microscopy to characterize the NLMR and the crystallinity of Nb-doped Cd$_3$As$_2$ thin films. We find surface-doped Nb-Cd$_3$As$_2$ thin films display a maximum NLMR around $B = 7$ T and bulk-doped Nb-Cd$_3$As$_2$ thin films display a maximum NLMR over $B = 9$ T--all while maintaining crystallinity. This is more than a 100% relative enhancement of the maximum NLMR compared to pristine Cd$_3$As$_2$ thin films ($B = 4$ T). Our work demonstrates the potential of high-energy ion implantation as a practical route to realize chiralitronic functionalities in topological semimetals.

cond-mat.mtrl-sci↗

arXiv2025

Spatiotemporal Mapping of Anisotropic Thermal Transport in GaN Thin Films via Ultrafast X-ray Diffraction

Efficient thermal management is essential for the reliability of modern power electronics, where increasing device density leads to severe heat dissipation challenges. However, in thin-film systems, thermal transport is often compromised by interfacial resistance and microscale defects introduced during synthesis or transfer, which are difficult to characterize using conventional techniques. Here we present a non-contact, spatiotemporal-resolved ultrafast x-ray diffraction method to extract in-plane thermal conductivity and thermal boundary conductance, using GaN thin films on silicon as a model system. By tracking the pump-induced lattice strain, we reconstruct the lateral heat flow dynamics and quantitatively probe thermal transport near a wrinkle defect. We uncover pronounced asymmetric heat dissipation across the wrinkle, with a four-fold reduction in the local thermal conductivity near the wrinkle and a 25% drop in interfacial conductance. Our work demonstrates that ultrafast x-ray diffraction can serve as a precise thermal metrology tool for characterizing heat transport in multilayered thin-film structures for next-generation microelectronic devices.

cond-mat.mtrl-sci↗

arXiv2025

A Foundation Model for Non-Destructive Defect Identification from Vibrational Spectra

Defects are ubiquitous in solids and strongly influence materials' mechanical and functional properties. However, non-destructive characterization and quantification of defects, especially when multiple types coexist, remain a long-standing challenge. Here we introduce DefectNet, a foundation machine learning model that predicts the chemical identity and concentration of substitutional point defects with multiple coexisting elements directly from vibrational spectra, specifically phonon density-of-states (PDoS). Trained on over 16,000 simulated spectra from 2,000 semiconductors, DefectNet employs a tailored attention mechanism to identify up to six distinct defect elements at concentrations ranging from 0.2% to 25%. The model generalizes well to unseen crystals across 56 elements and can be fine-tuned on experimental data. Validation using inelastic scattering measurements of SiGe alloys and MgB$_2$ superconductor demonstrates its accuracy and transferability. Our work establishes vibrational spectroscopy as a viable, non-destructive probe for point defect quantification in bulk materials, and highlights the promise of foundation models in data-driven defect engineering.

cond-mat.mtrl-sci↗

arXiv2025

Bridging the Language Gap: Enhancing Multilingual Prompt-Based Code Generation in LLMs via Zero-Shot Cross-Lingual Transfer

The use of Large Language Models (LLMs) for program code generation has gained substantial attention, but their biases and limitations with non-English prompts challenge global inclusivity. This paper investigates the complexities of multilingual prompt-based code generation. Our evaluations of LLMs, including CODELLAMA and CODEGEMMA, reveal significant disparities in code quality for non-English prompts; we also demonstrate the inadequacy of simple approaches like prompt translation, bootstrapped data augmentation, and fine-tuning. To address this, we propose a zero-shot cross-lingual approach using a neural projection technique, integrating a cross-lingual encoder like LASER to map multilingual embeddings from it into the LLM's token space. This method requires training only on English data and scales effectively to other languages. Results on a translated and quality-checked MBPP dataset show substantial improvements in code quality. This research promotes a more inclusive code generation landscape by empowering LLMs with multilingual capabilities to support the diverse linguistic spectrum in programming.

cs.CL↗

arXiv2025

Raman Fingerprints of Phase Transitions and Ferroic Couplings in Van der Waals Material CuCrP$_2$S$_6$

CuCrP$_2$S$_6$ (CCPS), a type-II multiferroic material, exhibits unique phase transitions involving ferroelectric, antiferroelectric, and antiferromagnetic ordering. In this study, we conduct a comprehensive investigation on the intricate phase transitions and their multiferroic couplings in CCPS across a wide temperature range from 4 to 345 K through Raman spectroscopic measurements down to 5 cm$^{-1}$. We first assign the observed Raman modes with the support of theoretical calculations and angle-resolved polarized Raman measurements. We further present clear signatures of phase transitions from analyses of temperature-dependent Raman spectral parameters. Particularly, two low-frequency soft modes are observed at 36.1 cm$^{-1}$ and 70.5 cm$^{-1}$ below 145 K, indicating the antiferroelectric to quasi-antiferroelectric transition. Moreover, phonon modes hardening is observed when the temperature increases from 4 to 65 K, suggesting negative thermal expansion (NTE) and strong magnetoelastic coupling below 65 K. These findings advance the understanding of vdW multiferroic CCPS, paving the way for future design and engineering of multiferroicity in cutting-edge technologies, such as spintronics and quantum devices.

cond-mat.mtrl-sci↗

arXiv2025

AI-Driven Defect Engineering for Advanced Thermoelectric Materials

Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming thermoelectric materials design. Advanced ML approaches including deep neural networks, graph-based models, and transformer architectures, integrated with high-throughput simulations and growing databases, effectively capture structure-property relationships in a complex multiscale defect space and overcome the curse of dimensionality. This review discusses AI-enhanced defect engineering strategies such as composition optimization, entropy and dislocation engineering, and grain boundary design, along with emerging inverse design techniques for generating materials with targeted properties. Finally, it outlines future opportunities in novel physics mechanisms and sustainability, highlighting the critical role of AI in accelerating the discovery of thermoelectric materials.

cond-mat.mtrl-sci↗

arXiv2025

Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models

Although Retrieval-Augmented Large Language Models (RALMs) demonstrate their superiority in terms of factuality, they do not consistently outperform the original retrieval-free Language Models (LMs). Our experiments reveal that this example-level performance inconsistency exists not only between retrieval-augmented and retrieval-free LM but also among different retrievers. To understand this phenomenon, we investigate the degeneration behavior of RALMs and theoretically decompose it into four categories. Further analysis based on our decomposition reveals that the innate difference in knowledge sources and the unpredictable degeneration of the reader model contribute most to the inconsistency. Drawing from our analysis, we introduce Ensemble of Retrievers (EoR), a trainable framework that can adaptively retrieve from different knowledge sources and effectively decrease unpredictable reader errors. Our experiments on Open Domain Question Answering show that EoR substantially improves performance over the RALM with a single retriever by considerably reducing inconsistent behaviors.

cs.AI↗

arXiv2025

TRACE: Temporal Grounding Video LLM via Causal Event Modeling

Video Temporal Grounding (VTG) is a crucial capability for video understanding models and plays a vital role in downstream tasks such as video browsing and editing. To effectively handle various tasks simultaneously and enable zero-shot prediction, there is a growing trend in employing video LLMs for VTG tasks. However, current video LLM-based methods rely exclusively on natural language generation, lacking the ability to model the clear structure inherent in videos, which restricts their effectiveness in tackling VTG tasks. To address this issue, this paper first formally introduces causal event modeling framework, which represents video LLM outputs as sequences of events, and predict the current event using previous events, video inputs, and textural instructions. Each event consists of three components: timestamps, salient scores, and textual captions. We then propose a novel task-interleaved video LLM called TRACE to effectively implement the causal event modeling framework in practice. The TRACE process visual frames, timestamps, salient scores, and text as distinct tasks, employing various encoders and decoding heads for each. Task tokens are arranged in an interleaved sequence according to the causal event modeling framework's formulation. Extensive experiments on various VTG tasks and datasets demonstrate the superior performance of TRACE compared to state-of-the-art video LLMs. Our model and code are available at https://github.com/gyxxyg/TRACE.

cs.CV↗