arXiv subjects

Mingqiang Li

Publications and source records attributed to Mingqiang Li.

At least 19 recordsLinked to original sources

arXiv2026

Identification and Prediction of Photoplasticity in Semiconductors Using Feature Engineering and Machine learning

Photoplasticity, the light-induced change in plastic deformation, plays a pivotal role in the mechanical durability and manufacturing of semiconductor materials. Yet, its governing mechanisms remain incompletely understood, owing to the interplay of coupled multiphysics factors. Here, we conduct high-throughput nanoindentation measurements to compile a dataset of paired hardness values in dark and light conditions. Then, we engineer physics-informed descriptors spanning electrical, mechanical, and optical properties, and identify the ten most informative features, including bandgap, breakdown field, and refractive index, to enable an interpretable machine learning framework that yields transferable design rules for light-tunable semiconductor mechanics. By identifying and predicting photoplasticity in semiconductors, this work provides a practical pathway for extracting mechanism-linked, transferable guidelines to engineer light-responsive mechanical behavior in semiconductor materials and devices.

cond-mat.mtrl-sci↗

arXiv2025

A Fast Anti-Jamming Cognitive Radar Deployment Algorithm Based on Reinforcement Learning

The fast deployment of cognitive radar to counter jamming remains a critical challenge in modern warfare, where more efficient deployment leads to quicker detection of targets. Existing methods are primarily based on evolutionary algorithms, which are time-consuming and prone to falling into local optima. We tackle these drawbacks via the efficient inference of neural networks and propose a brand new framework: Fast Anti-Jamming Radar Deployment Algorithm (FARDA). We first model the radar deployment problem as an end-to-end task and design deep reinforcement learning algorithms to solve it, where we develop integrated neural modules to perceive heatmap information and a brand new reward format. Empirical results demonstrate that our method achieves coverage comparable to evolutionary algorithms while deploying radars approximately 7,000 times faster. Further ablation experiments confirm the necessity of each component of FARDA.

cs.AI↗

arXiv2025

An AI-native experimental laboratory for autonomous biomolecular engineering

Autonomous scientific research, capable of independently conducting complex experiments and serving non-specialists, represents a long-held aspiration. Achieving it requires a fundamental paradigm shift driven by artificial intelligence (AI). While autonomous experimental systems are emerging, they remain confined to areas featuring singular objectives and well-defined, simple experimental workflows, such as chemical synthesis and catalysis. We present an AI-native autonomous laboratory, targeting highly complex scientific experiments for applications like autonomous biomolecular engineering. This system autonomously manages instrumentation, formulates experiment-specific procedures and optimization heuristics, and concurrently serves multiple user requests. Founded on a co-design philosophy of models, experiments, and instruments, the platform supports the co-evolution of AI models and the automation system. This establishes an end-to-end, multi-user autonomous laboratory that handles complex, multi-objective experiments across diverse instrumentation. Our autonomous laboratory supports fundamental nucleic acid functions-including synthesis, transcription, amplification, and sequencing. It also enables applications in fields such as disease diagnostics, drug development, and information storage. Without human intervention, it autonomously optimizes experimental performance to match state-of-the-art results achieved by human scientists. In multi-user scenarios, the platform significantly improves instrument utilization and experimental efficiency. This platform paves the way for advanced biomaterials research to overcome dependencies on experts and resource barriers, establishing a blueprint for science-as-a-service at scale.

cs.AI↗

arXiv2025

Cooperative Game-Theoretic Credit Assignment for Multi-Agent Policy Gradients via the Core

This work focuses on the credit assignment problem in cooperative multi-agent reinforcement learning (MARL). Sharing the global advantage among agents often leads to insufficient policy optimization, as it fails to capture the coalitional contributions of different agents. In this work, we revisit the policy update process from a coalitional perspective and propose CORA, an advantage allocation method guided by a cooperative game-theoretic core allocation. By evaluating the marginal contributions of different coalitions and combining clipped double Q-learning to mitigate overestimation bias, CORA estimates coalition-wise advantages. The core formulation enforces coalition-wise lower bounds on allocated credits, so that coalitions with higher advantages receive stronger total incentives for their participating agents, enabling the global advantage to be attributed to different coalition strategies and promoting coordinated optimal behavior. To reduce computational overhead, we employ random coalition sampling to approximate the core allocation efficiently. Experiments on matrix games, differential games, and multi-agent collaboration benchmarks demonstrate that our method outperforms baselines. These findings highlight the importance of coalition-level credit assignment and cooperative games for advancing multi-agent learning.

cs.MA↗

arXiv2025

Unveiling competitions between carrier recombination pathways in semiconductors via mechanical damping

The total rate of carrier recombination in semiconductors has conventionally been expressed using an additive model, r_total = \Sigma r_i , which rules out the interactions between carrier recombination pathways. Here we challenge this paradigm by demonstrating pathway competitions using our newly developed light-induced mechanical absorption spectroscopy (LIMAS), which allows us to probe genuine recombination dynamics in semiconductors via mechanical damping. We show that the total recombination rate in zinc sulfide (ZnS), a model semiconductor material, follows a multiplicative weighting model, r_total \propto \Pi r_i ^(w_i) with \Sigma w_i=1. Under both steady-state and switch-on illuminations, the weighting factors w_i for each recombination pathway-direct, trap-assisted, and sublinear-are dictated by the carrier generation mechanism: (i) interband transition favors direct recombination; (ii) single-defect level-mediated generation promotes trap-assisted recombination; (iii) generation involving multiple saturated defect levels gives rise to sublinear recombination. Upon light switch-off, localized state changes drive a dynamic evolution of w_i, altering pathway competitions. These findings reshape our fundamental understanding of carrier dynamics and provide a new strategy to optimize next-generation optoelectronic devices.

cond-mat.mtrl-sci↗

arXiv2025

Direct evidence and atomic-scale mechanisms of reduced dislocation mobility in an inorganic semiconductor under illumination

Photo-plasticity in semiconductors, wherein their mechanical properties such as strength, hardness and ductility are influenced by light exposure, has been reported for several decades. Although such phenomena have drawn significant attention for the manufacturability and usage of deformable semiconductor devices, their underlying mechanisms are not well understood due to the lack of direct evidence. Here we provide experimental observation and atomic insights into the reduced mobility of dislocations in zinc sulfide, as a model material, under light. Using photo-nanoindentation and transmission electron microscopy, we observe that dislocations glide shorter distances under light than those in darkness and there are no apparent deformation twins in both conditions. By atomic-scale simulations, we demonstrate that the decreased dislocation mobility is attributed to the increased Peierls stress for dislocation motion and enhanced stress fields around dislocation cores due to photoexcitation. This study improves the understanding of photo-plastic effects in inorganic semiconductors, offering the opportunities for modulating their mechanical properties using light.

cond-mat.mtrl-sci↗

arXiv2023

Molecular tuning of DNA framework-programmed silicification by cationic silica cluster attachment

The organizational complexity of biominerals has long fascinated scientists seeking to understand biological programming and implement new developments in biomimetic materials chemistry. Nonclassical crystallization pathways have been observed and analyzed in typical crystalline biominerals, involving the controlled attachment and reconfiguration of nanoparticles and clusters on organic templates. However, the understanding of templated amorphous silica mineralization remains limited, hindering the rational design of complex silica-based materials. Here, we present a systematic study on the stabilization of self-capping cationic silica cluster (CSC) and their assembly dynamics using DNA nanostructures as programmable attachment templates. By tuning the composition and structure of CSC, we demonstrate high-fidelity silicification at single-cluster resolution, revealing a process of adaptive templating involving cooperative adjustments of both the DNA framework and cluster morphology. Our results provide a unified model of silicification by cluster attachment and pave the way towards the molecular tuning of pre- and post-nucleation stages of sol-gel reactions. Overall, our findings provide new insights for the design of silica-based materials with controlled organization and functionality, bridging the gap between biomineralization principles and the rational design of biomimetic material.

physics.chem-ph↗

arXiv2023

Twisted DNA origami-based chiral monolayers for spin filtering

DNA monolayers with inherent chirality play a pivotal role across various domains, including biosensors, DNA chips, and bioelectronics. Nonetheless, conventional DNA chiral monolayers, typically constructed from single-stranded DNA (ssDNA) or double-stranded DNA (dsDNA), often lack structural orderliness and design flexibility at the interface. Structural DNA nanotechnology emerges as a promising solution to tackle these challenges. In this study, we present a strategy for crafting highly adaptable twisted DNA origami-based chiral monolayers. These structures exhibit distinct interfacial assembly characteristics and effectively mitigate the structural disorder of dsDNA monolayers, which is constrained by a limited persistence length of ~50 nm of dsDNA. We highlight the spin-filtering capabilities of four representative DNA origami-based chiral monolayers, demonstrating a maximal one-order-of-magnitude increase in spin-filtering efficiency per unit area compared to conventional dsDNA chiral monolayers. Intriguingly, our findings reveal that the higher-order, tertiary, chiral structure of twisted DNA origami further enhances the spin-filtering efficiency. This work paves the way for the rational design of DNA chiral monolayers.

physics.chem-ph↗

arXiv2023

Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learning

Federated learning is a distributed machine learning system that uses participants' data to train an improved global model. In federated learning, participants cooperatively train a global model, and they will receive the global model and payments. Rational participants try to maximize their individual utility, and they will not input their high-quality data truthfully unless they are provided with satisfactory payments based on their data quality. Furthermore, federated learning benefits from the cooperative contributions of participants. Accordingly, how to establish an incentive mechanism that both incentivizes inputting data truthfully and promotes stable cooperation has become an important issue to consider. In this paper, we introduce a data sharing game model for federated learning and employ game-theoretic approaches to design a core-selecting incentive mechanism by utilizing a popular concept in cooperative games, the core. In federated learning, the core can be empty, resulting in the core-selecting mechanism becoming infeasible. To address this, our core-selecting mechanism employs a relaxation method and simultaneously minimizes the benefits of inputting false data for all participants. However, this mechanism is computationally expensive because it requires aggregating exponential models for all possible coalitions, which is infeasible in federated learning. To address this, we propose an efficient core-selecting mechanism based on sampling approximation that only aggregates models on sampled coalitions to approximate the exact result. Extensive experiments verify that the efficient core-selecting mechanism can incentivize inputting high-quality data and stable cooperation, while it reduces computational overhead compared to the core-selecting mechanism.

cs.GT↗

arXiv2023

A-PSRO: A Unified Strategy Learning Method with Advantage Function for Normal-form Games

Solving Nash equilibrium is the key challenge in normal-form games with large strategy spaces, where open-ended learning frameworks offer an efficient approach. In this work, we propose an innovative unified open-ended learning framework A-PSRO, i.e., Advantage Policy Space Response Oracle, as a comprehensive framework for both zero-sum and general-sum games. In particular, we introduce the advantage function as an enhanced evaluation metric for strategies, enabling a unified learning objective for agents engaged in normal-form games. We prove that the advantage function exhibits favorable properties and is connected with the Nash equilibrium, which can be used as an objective to guide agents to learn strategies efficiently. Our experiments reveal that A-PSRO achieves a considerable decrease in exploitability in zero-sum games and an escalation in rewards in general-sum games, significantly outperforming previous PSRO algorithms.

cs.GT↗

arXiv2023

Harnessing dislocation motion using an electric field

Dislocations, line defects in crystalline materials, play an essential role in the mechanical[1,2], electrical[3], optical[4], thermal[5], and phase transition[6] properties of these materials. Dislocation motion, an important mechanism underlying crystal plasticity, is critical for the hardening, processing, and application of a wide range of structural and functional materials[1,7,8]. For decades, the movement of dislocations has been widely observed in crystalline solids under mechanical loading[9-11]. However, the goal of manipulating dislocation motion via a non-mechanical field alone remains elusive. Here, we present real-time observations of dislocation motion controlled solely by an external electric field in single-crystalline zinc sulfide (ZnS). We find that 30° partial dislocations can move back and forth depending on the direction of the electric field, while 90° partial dislocations are motionless. We reveal the nonstoichiometric nature of dislocation cores using atomistic imaging and determine their charge characteristics by density functional theory calculations. The glide barriers of charged 30° partial dislocations, which are lower than those of 90° partial dislocations, further decrease under an electric field, explaining the experimental observations. This study provides direct evidence of dislocation dynamics under a non-mechanical stimulus and opens up the possibility of modulating dislocation-related properties.

cond-mat.mtrl-sci↗

arXiv2023

Learning A Sparse Transformer Network for Effective Image Deraining

Transformers-based methods have achieved significant performance in image deraining as they can model the non-local information which is vital for high-quality image reconstruction. In this paper, we find that most existing Transformers usually use all similarities of the tokens from the query-key pairs for the feature aggregation. However, if the tokens from the query are different from those of the key, the self-attention values estimated from these tokens also involve in feature aggregation, which accordingly interferes with the clear image restoration. To overcome this problem, we propose an effective DeRaining network, Sparse Transformer (DRSformer) that can adaptively keep the most useful self-attention values for feature aggregation so that the aggregated features better facilitate high-quality image reconstruction. Specifically, we develop a learnable top-k selection operator to adaptively retain the most crucial attention scores from the keys for each query for better feature aggregation. Simultaneously, as the naive feed-forward network in Transformers does not model the multi-scale information that is important for latent clear image restoration, we develop an effective mixed-scale feed-forward network to generate better features for image deraining. To learn an enriched set of hybrid features, which combines local context from CNN operators, we equip our model with mixture of experts feature compensator to present a cooperation refinement deraining scheme. Extensive experimental results on the commonly used benchmarks demonstrate that the proposed method achieves favorable performance against state-of-the-art approaches. The source code and trained models are available at https://github.com/cschenxiang/DRSformer.

cs.CV↗

arXiv2022

Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring

We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring. Our method is motivated by the convolution theorem that the correlation or convolution of two signals in the spatial domain is equivalent to an element-wise product of them in the frequency domain. This inspires us to develop an efficient frequency domain-based self-attention solver (FSAS) to estimate the scaled dot-product attention by an element-wise product operation instead of the matrix multiplication in the spatial domain. In addition, we note that simply using the naive feed-forward network (FFN) in Transformers does not generate good deblurred results. To overcome this problem, we propose a simple yet effective discriminative frequency domain-based FFN (DFFN), where we introduce a gated mechanism in the FFN based on the Joint Photographic Experts Group (JPEG) compression algorithm to discriminatively determine which low- and high-frequency information of the features should be preserved for latent clear image restoration. We formulate the proposed FSAS and DFFN into an asymmetrical network based on an encoder and decoder architecture, where the FSAS is only used in the decoder module for better image deblurring. Experimental results show that the proposed method performs favorably against the state-of-the-art approaches. Code will be available at \url{https://github.com/kkkls/FFTformer}.

cs.CV↗

arXiv2022

Electric-field control of the nucleation and motion of isolated three-fold polar vertices

Recently various topological polar structures have been discovered in oxide thin films. Despite the increasing evidence of their switchability under electrical and/or mechanical fields, the dynamic property of isolated ones, which is usually required for applications such as data storage, is still absent. Here, we show the controlled nucleation and motion of isolated three-fold vertices under an applied electric field. At the PbTiO3/SrRuO3 interface, a two-unit-cell thick SrTiO3 layer provides electrical boundary conditions for the formation of three-fold vertices. Utilizing the SrTiO3 layer and in situ electrical testing system, we find that isolated three-fold vertices can move in a controllable and reversible manner with a velocity up to ~629 nm s-1. Microstructural evolution of the nucleation and propagation of isolated three-fold vertices is further revealed by phase-field simulations. This work demonstrates the ability to electrically manipulate isolated three-fold vertices, shedding light on the dynamic property of isolated topological polar structures.

cond-mat.mtrl-sci↗

arXiv2021

Dynamics of Polar Skyrmion Bubbles under Electric Fields

Room-temperature polar skyrmion bubbles that are recently found in oxide superlattice, have received enormous interests for their potential applications in nanoelectronics due to the nanometer size, emergent chirality, and negative capacitance. For practical applications, the ability to controllably manipulate them by using external stimuli is prerequisite. Here, we study the dynamics of individual polar skyrmion bubbles at the nanoscale by using in situ biasing in a scanning transmission electron microscope. The reversible electric field-driven phase transition between topological and trivial polar states are demonstrated. We create, erase and monitor the shrinkage and expansion of individual polar skyrmions. We find that their transition behaviors are substantially different from that of magnetic analogue. The underlying mechanism is discussed by combing with the phase-field simulations. The controllable manipulation of nanoscale polar skyrmions allows us to tune the dielectric permittivity at atomic scale and detailed knowledge of their phase transition behaviors provides fundamentals for their applications in nanoelectronics.

cond-mat.mes-hall↗

arXiv2021

Blocked and Hierarchical Disentangled Representation From Information Theory Perspective

We propose a novel and theoretical model, blocked and hierarchical variational autoencoder (BHiVAE), to get better-disentangled representation. It is well known that information theory has an excellent explanatory meaning for the network, so we start to solve the disentanglement problem from the perspective of information theory. BHiVAE mainly comes from the information bottleneck theory and information maximization principle. Our main idea is that (1) Neurons block not only one neuron node is used to represent attribute, which can contain enough information; (2) Create a hierarchical structure with different attributes on different layers, so that we can segment the information within each layer to ensure that the final representation is disentangled. Furthermore, we present supervised and unsupervised BHiVAE, respectively, where the difference is mainly reflected in the separation of information between different blocks. In supervised BHiVAE, we utilize the label information as the standard to separate blocks. In unsupervised BHiVAE, without extra information, we use the Total Correlation (TC) measure to achieve independence, and we design a new prior distribution of the latent space to guide the representation learning. It also exhibits excellent disentanglement results in experiments and superior classification accuracy in representation learning.

cs.IT↗

arXiv2019

Atomic-scale observations of electrical and mechanical manipulation of topological polar flux-closure

The ability to controllably manipulate the complex topological polar configurations, such as polar flux-closure via external stimuli, enables many applications in electromechanical devices and nanoelectronics including high-density information storage. Here, by using the atomically resolved in situ scanning transmission electron microscopy, we find that a polar flux-closure structure in PbTiO3/SrTiO3 superlattices films can be reversibly switched to ordinary mono ferroelectric c domain or a domain under electric field or stress. Specifically, the electric field initially drives the flux-closure move and breaks them to form intermediate a/c striped domains, while the mechanical stress firstly starts to squeeze the flux-closures to convert into small vortices at the interface and form a continues dipole wave. After the removal of the external stimuli, the flux-closure structure spontaneously returns. Our atomic study provides valuable insights into understanding the lattice-charge interactions and the competing interactions balance in these complex topological structures. Such reversible switching between the flux-closure and ordinary ferroelectric domains also provides the foundation for applications such as memories and sensors.

cond-mat.mtrl-sci↗

arXiv2019

Thickness-dependent in-plane polarization and structural phase transition in van der Waals Ferroelectric CuInP2S6

Van der Waals (vdW) layered materials have rather weaker interlayer bonding than the intra-layer bonding, therefore the exfoliation along the stacking direction enables the achievement of monolayer or few layers vdW materials with emerging novel physical properties and functionalities. The ferroelectricity in vdW materials recently attracts renewed interest for the potential use in high-density storage devices. As the thickness going thinner, the competition between the surface energy, depolarization field and interfacial chemical bonds may give rise to the modification of ferroelectricity and crystalline structure, which has limited investigations. In this work, combining the piezoresponse force microscope scanning, contact resonance imaging, we report the existence of the intrinsic in-plane polarization in vdW ferroelectrics CuInP2S6 (CIPS) single crystals, whereas below a critical thickness between 90-100 nm, the in-plane polarization disappears. The Young's modulus also shows an abrupt stiffness at the critical thickness. Based on the density functional theory calculations, we ascribe these behaviors to a structural phase transition from monoclinic to trigonal structure, which is further verified by transmission electron microscope technique. Taken together, these findings demonstrate the foundational importance of structural phase transition for enhancing the rich functionality and broad utility of vdW ferroelectrics.

cond-mat.mtrl-sci↗