arXiv subjects

Jian Luo

Publications and source records attributed to Jian Luo.

At least 19 recordsLinked to original sources

arXiv2026

Compositionally Complex Ceramics

The development of high-entropy ceramics (HECs) over the past decade has extended the high-entropy concept to a diverse range of oxides, borides, silicides, carbides, nitrides, fluorides, silicates, and other ceramic solid solutions, encompassing increasingly diverse crystal structures and bonding characteristics and exhibiting a broad spectrum of promising mechanical, thermal, and functional properties. Initial studies predominantly focused on five-component equimolar compositions, often assuming the formation of random solid solutions. More recently, 10-21 component ultrahigh-entropy ceramics have been developed as a subset of HECs, some of which exhibit intriguing abrupt phase transitions. In 2020, we proposed extending the exploration of HECs to the broader class of "compositionally complex ceramics" (CCCs), in which non-equimolar compositions and long- and short-range order reduce configurational entropy while providing additional opportunities to tailor and enhance materials properties, thereby outperforming their higher-entropy counterparts. Dual-phase CCCs have also been reported, with thermodynamic equilibria governing cation partitioning between the two phases and offering further opportunities to control and enhance properties through microstructural engineering. Subsequent studies have revealed grain-boundary phase-like transitions in CCCs that can control microstructural evolution and materials properties. Overall, CCCs offer a versatile platform for tailoring materials properties through diverse crystal structures and bonding characteristics, compositional complexity, non-equimolar designs, long- and short-range order, defects, and microstructural and interfacial engineering.

cond-mat.mtrl-sci↗

arXiv2026

Ultrahigh-Entropy Compositionally Complex Ceramics: Fluorite-Pyrochlore Phase Stability and Order-Disorder Transitions

Three groups of 32 ultrahigh-entropy compositionally complex ceramics (CCCs) containing 16-19 components were synthesized and characterized. The first group of 15 CCCs forms single ultrahigh-entropy fluorite or pyrochlore phases in 12 compositions, while two compositions exhibit fluorite-fluorite dual phases, observed for the first time. Compared with ternary A2B2O7 pyrochlore, these ultrahigh-entropy CCCs are more prone to ordering (pyrochlore formation), surprisingly even more at higher size disorder (more lattice distortion), with suppressed dual-phase formation relative to five-component high-entropy ceramics. Two new 19-component series, denoted as "19CCC-P|Fy" and "19CCC-P|Fz", form single ultrahigh-entropy fluorite or pyrochlore phases. The 19CCC-P|Fy series, maintaining a 2:2 ratio of 3+ and 4+ cations, exhibits a pyrochlore-to-fluorite order-disorder transition (ODT) at y ~ 0.84, consistent with the linear projection of the pyrochlore superstructure peak intensity as an order parameter. In contrast, the 19CCC-P|Fz series with non-2:2 ratios of 3+ and 4+ cations show an abrupt ODT at z ~ 0.41-0.44, despite a projected transition at z ~ 0.64 from the order parameter.

cond-mat.mtrl-sci↗

arXiv2026

Speech-Driven End-to-End Language Discrimination towards Chinese Dialects

Language discrimination among similar languages, varieties, and dialects is a challenging natural language processing task. The traditional text-driven focus leads to poor results. In this paper, we explore the effectiveness of speech-driven features towards language discrimination among Chinese dialects. First, we systematically explore the appropriateness of speech-driven MFCC features towards CNN-based language discrimination. Then, we design an end-to-end speech recognition model based on HMM-DNN to predict Chinese dialect words. We adopt attention to extract the discriminative words related to different Chinese dialects. Finally, through a CNN, we combine the word-level embedding and the MFCC-based features. Evaluation of two benchmark Chinese dialect corpora shows the appropriateness and effectiveness of the proposed speech-driven approach to fine-grained Chinese dialect discrimination compared to the state-of-the-art methods.

cs.CL↗

arXiv2026

Few-Shot Distribution-Aligned Flow Matching for Data Synthesis in Medical Image Segmentation

Data heterogeneity hinders clinical deployment of medical image analysis models, and generative data augmentation helps mitigate this issue. However, recent diffusion-based methods that synthesize image-mask pairs often ignore distribution shifts between generated and real images across scenarios, and such mismatches can markedly degrade downstream performance. To address this issue, we propose AlignFlow, a flow matching model that aligns with the target reference image distribution via differentiable reward fine-tuning, and remains effective even when only a small number of reference images are provided. Specifically, we divide the training of the flow matching model into two stages: in the first stage, the model fits the training data to generate plausible images; Then, we introduce a distribution alignment mechanism and employ differentiable reward to steer the generated images toward the distribution of the given samples from the target domain. In addition, to enhance the diversity of generated masks, we also design a flow matching based mask generation to complement the diversity in regions of interest. Extensive experiments demonstrate the effectiveness of our approach, i.e., performance improvement by 3.5-4.0% in mDice and 3.5-5.6% in mIoU across a variety of datasets and scenarios.

eess.IV↗

arXiv2026

Ultrafast Sintering

This Perspective critically assesses recent advances in ultrafast sintering and highlights open scientific questions and emerging technological opportunities. Mechanistic studies of flash sintering indicate that the flash event initiates as a coupled thermal and electrical runaway, while rapid densification is enabled by ultrahigh heating rates and elevated sintering temperatures. Building on this understanding, ultrafast sintering has been realized without passing electric currents through the specimens via multiple approaches, including rapid thermal annealing (using intense infrared heating), ultrafast high-temperature sintering (in which specimens are sandwiched between graphite felt heaters), blacklight sintering (employing blue laser or intense ultraviolet irradiation), atmospheric-pressure plasma sintering, and induction ultrafast sintering (utilizing skin currents in direct induction heating or no current in the specimens in susceptor-heating mode). Reactive ultrafast synthesis and sintering have also been demonstrated. Although several hypotheses have been proposed, the mechanisms governing ultrafast sintering and its kinetics warrant further investigation. In particular, reactive ultrafast synthesis and sintering of compositionally complex ceramics are scientifically intriguing to understand while also presenting technological opportunities. The expanding range of ultrafast sintering methods provides a versatile platform for high-throughput materials discovery, especially in the rapidly growing field of high-entropy and compositionally complex ceramics, which feature vast compositional spaces to explore.

cond-mat.mtrl-sci↗

arXiv2026

ReCreate: Reasoning and Creating Domain Agents Driven by Experience

Large Language Model agents are reshaping the industrial landscape. However, most practical agents remain human-designed because tasks differ widely, making them labor-intensive to build. This situation poses a central question: can we automatically create and adapt domain agents in the wild? While several recent approaches have sought to automate agent creation, they typically treat agent generation as a black-box procedure and rely solely on final performance metrics to guide the process. Such strategies overlook critical evidence explaining why an agent succeeds or fails, and often require high computational costs. To address these limitations, we propose ReCreate, an experience-driven framework for the automatic creation of domain agents. ReCreate systematically leverages agent interaction histories, which provide rich concrete signals on both the causes of success or failure and the avenues for improvement. Specifically, we introduce an agent-as-optimizer paradigm that effectively learns from experience via three key components: (i) an experience storage and retrieval mechanism for on-demand inspection; (ii) a reasoning-creating synergy pipeline that maps execution experience into scaffold edits; and (iii) hierarchical updates that abstract instance-level details into reusable domain patterns. In experiments across diverse domains, ReCreate consistently outperforms human-designed agents and existing automated agent generation methods, even when starting from minimal seed scaffolds.

cs.AI↗

arXiv2026

Fabrication of Dense Ultrafine-Grained MoW, MoWNb, and MoWNbTa Alloys: Influence of Cobalt Doping on Sintering and Grain Growth

Dense ultrafine-grained (UFG) refractory MoW, MoWNb, and MoWNbTa alloys were fabricated by combining high-energy ball milling (HEBM) and spark plasma sintering (SPS), achieving ~92-96% relative densities and ~70-180 nm grain sizes. The effects of 2 at.% cobalt (Co) addition on sintering behavior and high-temperature grain growth resistance were investigated as a function of compositional complexity. Activated sintering was observed, with 2 at.% Co addition increasing relative densities from ~92-96% to ~96-98%. Isothermal grain growth experiments at 1200 {\deg}C and 1300 {\deg}C showed that Co doping suppressed the relative grain growth rate, despite a modest initial grain size increase due to Co-activated sintering, with the effect becoming more pronounced in compositionally complex alloys. The observed trend is consistent with the recently proposed high-entropy grain boundary (HEGB) effect. Notably, Mo24.5W24.5Nb24.5Ta24.5Co2 achieved a 96.4% relative density and maintained an ultrafine grain size, increasing only slightly from ~122 nm to ~127 nm after 5 h annealing at 1200 {\deg}C. Scanning transmission electron microscopy (STEM and energy-dispersive X-ray spectroscopy (EDS) confirmed strong Co segregation at grain boundaries, accompanied by minor depletion of Ta and W, supporting a recently proposed grain boundary segregation model for high-entropy alloys and HEGBs.

cond-mat.mtrl-sci↗

arXiv2025

STNet: Spectral Transformation Network for Solving Operator Eigenvalue Problem

Operator eigenvalue problems play a critical role in various scientific fields and engineering applications, yet numerical methods are hindered by the curse of dimensionality. Recent deep learning methods provide an efficient approach to address this challenge by iteratively updating neural networks. These methods' performance relies heavily on the spectral distribution of the given operator: larger gaps between the operator's eigenvalues will improve precision, thus tailored spectral transformations that leverage the spectral distribution can enhance their performance. Based on this observation, we propose the Spectral Transformation Network (STNet). During each iteration, STNet uses approximate eigenvalues and eigenfunctions to perform spectral transformations on the original operator, turning it into an equivalent but easier problem. Specifically, we employ deflation projection to exclude the subspace corresponding to already solved eigenfunctions, thereby reducing the search space and avoiding converging to existing eigenfunctions. Additionally, our filter transform magnifies eigenvalues in the desired region and suppresses those outside, further improving performance. Extensive experiments demonstrate that STNet consistently outperforms existing learning-based methods, achieving state-of-the-art performance in accuracy.

cs.LG↗

arXiv2025

Scheduling Your LLM Reinforcement Learning with Reasoning Trees

Using Reinforcement Learning with Verifiable Rewards (RLVR) to optimize Large Language Models (LLMs) can be conceptualized as progressively editing a query's `Reasoning Tree'. This process involves exploring nodes (tokens) and dynamically modifying the model's policy at each node. When combined with data scheduling, this process yields further gains in data efficiency and accuracy. However, existing RLVR data scheduling methods typically rely on path-based metrics to rank queries, overlooking the reasoning tree structures of these queries. In this paper, we introduce a novel metric, namely Reasoning Score (r-score), which measures the query's learning difficulty based on the structure of its reasoning tree. Based on the r-score, we propose the Reasoning Tree Schedule (Re-Schedule), a scheduling algorithm that constructs a curriculum progressing from structurally simple (high r-score) to complex (low r-score) queries. Experiments on six math-reasoning benchmarks show that Re-Schedule significantly improves average accuracy, achieving gains of up to 3.2%. These strong results validate our approach and demonstrate that a structural understanding of the reasoning tree provides a more powerful and principled foundation for RLVR data scheduling.

cs.AI↗

arXiv2025

Accelerating Eigenvalue Dataset Generation via Chebyshev Subspace Filter

Eigenvalue problems are among the most important topics in many scientific disciplines. With the recent surge and development of machine learning, neural eigenvalue methods have attracted significant attention as a forward pass of inference requires only a tiny fraction of the computation time compared to traditional solvers. However, a key limitation is the requirement for large amounts of labeled data in training, including operators and their eigenvalues. To tackle this limitation, we propose a novel method, named Sorting Chebyshev Subspace Filter (SCSF), which significantly accelerates eigenvalue data generation by leveraging similarities between operators -- a factor overlooked by existing methods. Specifically, SCSF employs truncated fast Fourier transform sorting to group operators with similar eigenvalue distributions and constructs a Chebyshev subspace filter that leverages eigenpairs from previously solved problems to assist in solving subsequent ones, reducing redundant computations. To the best of our knowledge, SCSF is the first method to accelerate eigenvalue data generation. Experimental results show that SCSF achieves up to a 3.5 times speedup compared to various numerical solvers.

cs.LG↗

arXiv2025

Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective

Reinforcement Learning with Verifiable Rewards (RLVR) serves as a cornerstone technique for enhancing the reasoning capabilities of Large Language Models (LLMs). However, its training is often plagued by \emph{entropy collapse}, a rapid decline in policy entropy that limits exploration and undermines training effectiveness. While recent works attempt to mitigate this issue via several heuristic entropy interventions, the underlying mechanisms remain poorly understood. In this work, we conduct comprehensive theoretical and empirical analyses of entropy dynamics in RLVR, offering two main insights: (1) We derive a tight analytical approximation for token-level entropy change at each update step, revealing four governing factors and providing a unified theoretical framework to explain how existing methods influence entropy; (2) We reveal a fundamental limitation of recent approaches: they rely on heuristic adjustments to one or two of these factors, leaving other relevant factors unconsidered, thus inherently limiting their effectiveness. Motivated by these findings, we propose STEER, a principled entropy-modulation method that adaptively reweights tokens based on theoretically-estimated entropy variations. Extensive experiments across six mathematical reasoning and three coding benchmarks demonstrate that STEER effectively mitigates entropy collapse and consistently outperforms state-of-the-art baselines.

cs.LG↗

arXiv2025

From High-Entropy Ceramics (HECs) to Compositionally Complex Ceramics (CCCs) and Beyond

Over the past decade, the field of high-entropy ceramics (HECs) has expanded rapidly to encompass a broad range of oxides, borides, silicides, and other ceramic solid solutions. In 2020, we proposed extending HECs to compositionally complex ceramics (CCCs), where non-equimolar compositions and the presence of long- or short-range order, although reducing configurational entropy, create new opportunities to tailor and enhance properties, often surpassing those of higher-entropy counterparts. Along these lines, several fundamental scientific questions arise. Is the entropy in HECs truly high? Is maximizing entropy always desirable? In this perspective article, I revisit key concepts and terminologies and highlight emerging directions, including dual-phase CCCs, ultrahigh-entropy phases, and novel processing routes such as ultrafast reactive sintering. I propose that exploring compositional complexity across vast non-equimolar spaces, together with exploiting correlated disorder (coupled chemical and structural short-range order), represents a transformative strategy for designing ceramics with superior performance.

cond-mat.mtrl-sci↗

arXiv2025

Can Data-Driven Dynamics Reveal Hidden Physics? There Is A Need for Interpretable Neural Operators

Recently, neural operators have emerged as powerful tools for learning mappings between function spaces, enabling data-driven simulations of complex dynamics. Despite their successes, a deeper understanding of their learning mechanisms remains underexplored. In this work, we classify neural operators into two types: (1) Spatial domain models that learn on grids and (2) Functional domain models that learn with function bases. We present several viewpoints based on this classification and focus on learning data-driven dynamics adhering to physical principles. Specifically, we provide a way to explain the prediction-making process of neural operators and show that neural operator can learn hidden physical patterns from data. However, this explanation method is limited to specific situations, highlighting the urgent need for generalizable explanation methods. Next, we show that a simple dual-space multi-scale model can achieve SOTA performance and we believe that dual-space multi-spatio-scale models hold significant potential to learn complex physics and require further investigation. Lastly, we discuss the critical need for principled frameworks to incorporate known physics into neural operators, enabling better generalization and uncovering more hidden physical phenomena.

cs.LG↗

arXiv2025

Temperature-insensitive fused-tapered fiber couplers based on negative thermal expansion material coating

A new method based on negative thermal expansion material coating is proposed to realize temperature insensitive fiber coupler. By coating a layer of modified epoxy resin with a negative thermal expansion coefficient onto the coupling region of fiber coupler, a stable splitting ratio over a wide temperature range can be achieved. A finite-element model for simulating the influence of thermal fluctuations on fused-tapered fiber coupler's splitting ratio is built and verified via experimental test. Furthermore, using this model, the influence of the thickness, length, and thermal expansion coefficient of the coating material on the splitting ratio is studied. Through adjusting the parameters of the coating, the temperature stability of the fiber coupler splitting ratio can be improved by more than one order of magnitude and improved to 1.2*10-5/K. The temperature-insensitive fused-tapered fiber coupler can find important application in optical precision measurement under extreme temperature environment, such as inter-satellite laser interferometry and high-precision fiber gyroscopes.

physics.optics↗

arXiv2024

Surface molecular engineering to enable processing of sulfide solid electrolytes in humid ambient air

Sulfide solid state electrolytes are promising candidates to realize all solid state batteries due to their superior ionic conductivity and excellent ductility. However, their hypersensitivity to moisture requires processing environments that are not compatible with todays lithium ion battery manufacturing infrastructure. Herein, we present a reversible surface modification strategy that enables the processability of sulfide SSEs under humid ambient air. We demonstrate that a long chain alkyl thiol, undecanethiol, is chemically compatible with the electrolyte with negligible impact on its ion conductivity. Importantly, the thiol modification extends the amount of time that the sulfide SSE can be exposed to air with 33 percent relative humidity with limited degradation of its structure while retaining a conductivity of above 1 mS per cm for up to 2 days, a more than 100 fold improvement in protection time over competing approaches. Experimental and computational results reveal that the thiol group anchors to the SSE surface, while the hydrophobic hydrocarbon tail provides protection by repelling water. The modified Li6PS5Cl SSE maintains its function after exposure to ambient humidity when implemented in a Li0.5In LiNi0.8Co0.1Mn0.1O2 ASSB. The proposed protection strategy based on surface molecular interactions represents a major step forward towards cost competitive and energy efficient sulfide SSE manufacturing for ASSB applications.

cond-mat.mtrl-sci↗

arXiv2024

A Machine Learning Framework for the Prediction of Grain Boundary Segregation in Chemically Complex Environments

The discovery of complex concentrated alloys has unveiled materials with diverse atomic environments, prompting the exploration of solute segregation beyond dilute alloys. Data-driven methods offer promising for modeling segregation in such chemically complex environments, and are employed in this study to understand segregation behavior of a refractory complex concentrated alloy, NbMoTaW. A flexible methodology is developed that uses composable computational modules, with different arrangements of these modules employed to obtain site availabilities at absolute zero and the corresponding density of states beyond the dilute limit, resulting in an extremely large dataset containing 10 million data points. The artificial neural network developed here can rely solely on descriptions of local atomic environments to predict behavior at the dilute limit with very small errors, while the addition of negative segregation instance classification allows any solute concentration from zero up to the equiatomic concentration for ternary or quaternary alloys to be modeled at room temperature. The machine learning model thus achieves a significant speed advantage over traditional atomistic simulations, being four orders of magnitude faster, while only experiencing a minimal reduction in accuracy. This efficiency presents a powerful tool for rapid microstructural and interfacial design in unseen domains. Scientifically, our approach reveals a transition in the segregation behavior of Mo from unfavorable in simple systems to favorable in complex environments. Additionally, increasing solute concentration was observed to cause anti-segregation sites to begin to fill, challenging conventional understanding and highlighting the complexity of segregation dynamics in chemically complex environments.

cond-mat.mtrl-sci↗

arXiv2024

Grain Boundary Segregation Models for High-Entropy Alloys: Theoretical Formulation and Derived Analytical Expressions to Elucidate High-Entropy Grain Boundaries

Grain boundary (GB) segregation models are derived for multi-principal element and high-entropy alloys (MPEAs and HEAs). Differing from classical models where one component is taken as solvent and others are considered solutes, these models are referenced to the bulk composition to enable improved treatments of MPEAs and HEAs with no principal components. An ideal solution model is first formulated and solved to obtain analytical expressions that predict GB segregation and GB energy in MPEAs and HEAs. A regular solution model is further derived. The GB composition calculated using the simple analytical expression derived in this study and data from the Materials Project agree well with a prior sphosipcated atomistic simulation for NbMoTaW. The simplicity of the derived analytical expressions makes them useful for not only conveniently predicting GB segregation trends in HEAs, but also analyzing nascent interfacial phenomena in composionally complex GBs. As an application example, the derived models are used to further formulate a set of useful equations to elucidate an emergent concept of high-entropy grain boundaries (HEGBs).

cond-mat.mtrl-sci↗

arXiv2024

Accelerating PDE Data Generation via Differential Operator Action in Solution Space

Recent advancements in data-driven approaches, such as Neural Operator (NO), have demonstrated their effectiveness in reducing the solving time of Partial Differential Equations (PDEs). However, one major challenge faced by these approaches is the requirement for a large amount of high-precision training data, which needs significant computational costs during the generation process. To address this challenge, we propose a novel PDE dataset generation algorithm, namely Differential Operator Action in Solution space (DiffOAS), which speeds up the data generation process and enhances the precision of the generated data simultaneously. Specifically, DiffOAS obtains a few basic PDE solutions and then combines them to get solutions. It applies differential operators on these solutions, a process we call 'operator action', to efficiently generate precise PDE data points. Theoretical analysis shows that the time complexity of DiffOAS method is one order lower than the existing generation method. Experimental results show that DiffOAS accelerates the generation of large-scale datasets with 10,000 instances by 300 times. Even with just 5% of the generation time, NO trained on the data generated by DiffOAS exhibits comparable performance to that using the existing generation method, which highlights the efficiency of DiffOAS.

cs.LG↗