arXiv subjects

Zhiqiang Zhao

Publications and source records attributed to Zhiqiang Zhao.

At least 19 recordsLinked to original sources

arXiv2026

Machine-learned atomistic simulations reveal the basis of hydrogen-induced crack-plane transition in alpha-Fe

Hydrogen-related fracture in body-centered cubic Fe and ferritic steels often appears as transgranular quasi-cleavage rather than purely intergranular failure, especially at low to moderate hydrogen contents. Fractography has suggested that hydrogen may change the dominant cleavage faceting from {100} toward {110}, but atomic-scale evidence for this possible crack-plane transition remains unclear. Here we construct an efficient neural-network potential for α-Fe/H and combine large-scale, three-dimensional molecular dynamics with grand-canonical Monte Carlo (GCMC), allowing the near-tip crack-surface region and crack tip within a defined GCMC domain to exchange hydrogen with a reservoir at fixed chemical potential. A comparison of four crack systems identifies the controlling response: (100)[010], (100)[011], and (110)[001] remain cleavage-dominated, whereas the (110)[1-10] crack changes from dislocation emission in pure Fe to cleavage under hydrogen charging. The energetic origin is twofold. Hydrogen lowers the Griffith cleavage threshold of the {110} cleavage-plane family more strongly than that of {100}, and, for the controlling crack, a Rice-type energetic descriptor indicates that the surface-energy-controlled cleavage resistance decreases faster than the unstable-stacking-fault-controlled emission resistance, consistent with a weakened dislocation-emission shield. These results provide a thermodynamically consistent atomistic basis for a hydrogen-induced transgranular crack-plane transition in Fe.

physics.comp-ph↗

arXiv2025

Dual Role of Nb in Defect-Mediated Strength and Ductility of γ-TiAl Alloys

The origin of the superior high-temperature strength of γ-TiAl with high Nb addition remains highly controversial, largely due to the unclear role of Nb atoms. Using large-scale hybrid Monte Carlo and molecular dynamics simulations with a self-developed neural network potential,we show that Nb atoms predominantly occupy Ti sites and form short-range order with neighboring Al atoms, but a non-negligible fraction also occupies Al sites (NbAl) and promotes the formation of antisite defects (TiAl). Both the NbAl and TiAl antisites exceptionally reduce stacking fault energies and facilitate deformation twinning, thereby enhancing plasticity. Meanwhile, these substitutional and antisite defects also increase the Peierls stress of both screw and edge dislocations, which hinders dislocation motion to cause pronounced solid-solution strengthening. This work provides mechanistic insights into the dual role of Nb in enhancing both strength and ductility in γ-TiAl and further offers guidance for defect and composition engineering in advanced alloy systems.

cond-mat.mtrl-sci↗

arXiv2025

Achieving Empirical Potential Efficiency with DFT Accuracy: A Neuroevolution Potential for the $α$-Fe--C--H System

A neuroevolution potential (NEP) for the ternary $α$-Fe--C--H system was developed based on a database generated from spin-polarized density functional theory (DFT) calculations, achieving empirical potential efficiency with DFT accuracy. At the same power consumption, simulation speeds using NEP are comparable to, or even faster than, those with bond order potentials. The NEP achieves DFT-level accuracy across a wide range of scenarios commonly encountered in studies of $α$-Fe- and $α$-Fe--C under hydrogen environments. The NEP enables large-scale atomistic simulations with DFT-level accuracy at the cost of empirical potentials, offering a practical tool to study hydrogen embrittlement in steel.

cond-mat.mtrl-sci↗

arXiv2025

Anomalous strain-dependent thermal conductivity in superelastic screw-dislocated graphites

The design of strain-stable, or even strain-enhanced thermal transport materials is critical for stable operation of high-performance electronic devices. However, most nanomaterials suffer from strain-induced degradation, with even minor tensile strains markedly reducing thermal conductivity. Here, we demonstrate that screw-dislocated graphites (SDGs), recently identified as topological semimetals, display an unusual increase in cross-plane thermal conductivity under both tensile and compressive strains, revealed by high-accuracy machine-learning-potential-driven non-equilibrium molecular dynamics. Notably, SDGs exhibit over 100% enhancement under tensile strains up to 80% along the dislocation axis, arising from strain-induced increase in dislocation interface tilt angle that elongates the effective heat transfer paths. Their thermal conductivity surpasses multilayer graphene by an order of magnitude. An analytical model is further derived linking thermal conductivity to dislocation number and strain, offering a predictive framework for designing strain-tunable screwdislocated structures. These findings highlight SDGs as a promising platform for high-performance electronic and wearable devices with tunable thermal properties.

cond-mat.mtrl-sci↗

arXiv2025

Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning

Tokenization methods like Byte-Pair Encoding (BPE) enhance computational efficiency in large language models (LLMs) but often obscure internal character structures within tokens. This limitation hinders LLMs' ability to predict precise character positions, which is crucial in tasks like Chinese Spelling Correction (CSC) where identifying the positions of misspelled characters accelerates correction processes. We propose Token Internal Position Awareness (TIPA), a method that significantly improves models' ability to capture character positions within tokens by training them on reverse character prediction tasks using the tokenizer's vocabulary. Experiments demonstrate that TIPA enhances position prediction accuracy in LLMs, enabling more precise identification of target characters in original text. Furthermore, when applied to downstream tasks that do not require exact position prediction, TIPA still boosts performance in tasks needing character-level information, validating its versatility and effectiveness.

cs.CL↗

arXiv2025

Large Language Models as AI Agents for Digital Atoms and Molecules: Catalyzing a New Era in Computational Biophysics

In computational biophysics, where molecular data is expanding rapidly and system complexity is increasing exponentially, large language models (LLMs) and agent-based systems are fundamentally reshaping the field. This perspective article examines the recent advances at the intersection of LLMs, intelligent agents, and scientific computation, with a focus on biophysical computation. Building on these advancements, we introduce ADAM (Agent for Digital Atoms and Molecules), an innovative multi-agent LLM-based framework. ADAM employs cutting-edge AI architectures to reshape scientific workflows through a modular design. It adopts a hybrid neural-symbolic architecture that combines LLM-driven semantic tools with deterministic symbolic computations. Moreover, its ADAM Tool Protocol (ATP) enables asynchronous, database-centric tool orchestration, fostering community-driven extensibility. Despite the significant progress made, ongoing challenges call for further efforts in establishing benchmarking standards, optimizing foundational models and agents, building an open collaborative ecosystem and developing personalized memory modules. ADAM is accessible at https://sidereus-ai.com.

physics.comp-ph↗

arXiv2025

A high-efficiency neuroevolution potential for tobermorite and calcium silicate hydrate systems with ab initio accuracy

Tobermorite and Calcium Silicate Hydrate (C-S-H) systems are indispensable cement materials but still lack a satisfactory interatomic potential with both high accuracy and high computational efficiency for better understanding their mechanical performance. Here, we develop a Neuroevolution Machine Learning Potential (NEP) with Ziegler-Biersack-Littmark hybrid framework for tobermorite and C-S-H systems, which conveys unprecedented efficiency in molecular dynamics simulations with substantially reduced training datasets. Our NEP model achieves prediction accuracy comparable to DFT calculations using just around 300 training structures, significantly fewer than other existing machine learning potentials trained for tobermorite. Critically, the GPU-accelerated NEP computations enable scalable simulations of large tobermorite systems, reaching several thousand atoms per GPU card with high efficiency. We demonstrate the NEP's versatility by accurately predicting mechanical properties, phonon density of states, and thermal conductivity of tobermorite. Furthermore, we extend the NEP application to large-scale simulations of amorphous C-S-H, highlighting its potential for comprehensive analysis of structural and mechanical behaviors under various realistic conditions.

cond-mat.mes-hall↗

arXiv2025

Combining Relevance and Magnitude for Resource-Aware DNN Pruning

Pruning neural networks, i.e., removing some of their parameters whilst retaining their accuracy, is one of the main ways to reduce the latency of a machine learning pipeline, especially in resource- and/or bandwidth-constrained scenarios. In this context, the pruning technique, i.e., how to choose the parameters to remove, is critical to the system performance. In this paper, we propose a novel pruning approach, called FlexRel and predicated upon combining training-time and inference-time information, namely, parameter magnitude and relevance, in order to improve the resulting accuracy whilst saving both computational resources and bandwidth. Our performance evaluation shows that FlexRel is able to achieve higher pruning factors, saving over 35% bandwidth for typical accuracy targets.

cs.LG↗

arXiv2024

General-purpose machine-learned potential for 16 elemental metals and their alloys

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a feasible approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices. Our unified UNEP-v1 model exhibits superior performance across various physical properties compared to a widely used embedded-atom method potential, while maintaining remarkable efficiency. We demonstrate our approach's effectiveness through reproducing experimentally observed chemical order and stable phases, and large-scale simulations of plasticity and primary radiation damage in MoTaVW alloys. This work represents a significant leap towards a unified general-purpose MLP encompassing the periodic table, with profound implications for materials science.

cond-mat.mtrl-sci↗

arXiv2024

Student self-management, academic achievement: Exploring the mediating role of self-efficacy and the moderating influence of gender insights from a survey conducted in 3 universities in America

Excellent students are not only those who master more effective and efficient learning techniques to acquire and apply information. Even in the absence of correct learning, they are able to self-motivate, evaluate, and adjust their behavior. This study aims to explore the relationship between student self-management and academic achievement, with a focus on investigating the mediating role of self-efficacy and the moderating influence of gender in this relationship. A total of 289 students from three universities in the United States participated in this research. The results of the study indicate that students' level of self-management is positively correlated with their academic achievement, with self-efficacy playing a mediating role in this relationship and gender exerting a certain moderating effect. This study provides important insights into understanding the relationship between student self-management and academic achievement and supports the crucial role of educational leaders in educational practice.

cs.CY↗

arXiv2024

Exploring the Path of Transformation and Development for Study Abroad Consultancy Firms in China

In recent years, with the changing landscape of international education and the growing demand from Chinese students, study abroad consultancy firms in China need to adopt transformational development strategies to address challenges and maintain competitiveness. This study investigated the relationships between key performance indicators and several factors through a questionnaire survey of 158 consultancy firms. The factors examined included service diversification, technology adoption, talent management, and regulatory compliance. Descriptive statistical analysis was employed to analyze the data. The results showed that service scope diversification was positively correlated with firm performance. Technology adoption was positively correlated with operational efficiency. Talent management was positively correlated with service quality. Regulatory compliance was positively correlated with firm reputation. Consultancy firms that took progressive approaches in diversifying services, adopting new technologies, cultivating talent, and ensuring compliance demonstrated superior performance, efficiency, quality, and reputation compared to their less innovative counterparts. This research provides empirical evidence to support the transformation of Chinese study abroad consultancy firms. It also highlights the need for future studies to consider causality and contextual variations to gain deeper insights into this issue.

cs.CY↗

arXiv2024

A general-purpose neural network potential for Ti-Al-Nb alloys towards large-scale molecular dynamics with ab initio accuracy

High Nb-containing TiAl alloys exhibit exceptional high-temperature strength and room-temperature ductility, making them widely used in hot-section components of automotive and aerospace engines. However, the lack of accurate interatomic interaction potentials for large-scale modeling severely hampers a comprehensive understanding of the failure mechanism of Ti-Al-Nb alloys and the development of strategies to enhance the mechanical properties. Here, we develop a general-purpose machine-learned potential (MLP) for the Ti-Al-Nb ternary system by combining the neural evolution potentials framework with an active learning scheme. The developed MLP, trained on extensive first-principles datasets, demonstrates remarkable accuracy in predicting various lattice and defect properties, as well as high-temperature characteristics such as thermal expansion and melting point for TiAl systems. Notably, this potential can effectively describe the key effect of Nb doping on stacking fault energies and formation energies. Of practical importance is that our MLP enables large-scale molecular dynamics simulations involving tens of millions of atoms with ab initio accuracy, achieving an outstanding balance between computational speed and accuracy. These results pave the way for studying micro-mechanical behaviors in TiAl lamellar structures and developing high-performance TiAl alloys towards applications at elevated temperatures.

cond-mat.mtrl-sci↗

arXiv2023

Privacy-Preserving Electricity Theft Detection based on Blockchain

In most electricity theft detection schemes, consumers' power consumption data is directly input into the detection center. Although it is valid in detecting the theft of consumers, the privacy of all consumers is at risk unless the detection center is assumed to be trusted. In fact, it is impractical. Moreover, existing schemes may result in some security problems, such as the collusion attack due to the presence of a trusted third party, and malicious data tampering caused by the system operator (SO) being attacked. Aiming at the problems above, we propose a blockchain-based privacy-preserving electricity theft detection scheme without a third party. Specifically, the proposed scheme uses an improved functional encryption scheme to enable electricity theft detection and load monitoring while preserving consumers' privacy; distributed storage of consumers' data with blockchain to resolve security problems such as data tampering, etc. Meanwhile, we build a long short-term memory network (LSTM) model to perform higher accuracy for electricity theft detection. The proposed scheme is evaluated in a real environment, and the results show that it is more accurate in electricity theft detection within acceptable communication and computational overhead. Our system analysis demonstrates that the proposed scheme can resist various security attacks and preserve consumers' privacy.

cs.CR↗

arXiv2023

SF-SGL: Solver-Free Spectral Graph Learning from Linear Measurements

This work introduces a highly-scalable spectral graph densification framework (SGL) for learning resistor networks with linear measurements, such as node voltages and currents. We show that the proposed graph learning approach is equivalent to solving the classical graphical Lasso problems with Laplacian-like precision matrices. We prove that given $O(\log N)$ pairs of voltage and current measurements, it is possible to recover sparse $N$-node resistor networks that can well preserve the effective resistance distances on the original graph. In addition, the learned graphs also preserve the structural (spectral) properties of the original graph, which can potentially be leveraged in many circuit design and optimization tasks. To achieve more scalable performance, we also introduce a solver-free method (SF-SGL) that exploits multilevel spectral approximation of the graphs and allows for a scalable and flexible decomposition of the entire graph spectrum (to be learned) into multiple different eigenvalue clusters (frequency bands). Such a solver-free approach allows us to more efficiently identify the most spectrally-critical edges for reducing various ranges of spectral embedding distortions. Through extensive experiments for a variety of real-world test cases, we show that the proposed approach is highly scalable for learning sparse resistor networks without sacrificing solution quality. We also introduce a data-driven EDA algorithm for vectorless power/thermal integrity verifications to allow estimating worst-case voltage/temperature (gradient) distributions across the entire chip by leveraging a few voltage/temperature measurements.

cs.LG↗

arXiv2021

HyperSF: Spectral Hypergraph Coarsening via Flow-based Local Clustering

Hypergraphs allow modeling problems with multi-way high-order relationships. However, the computational cost of most existing hypergraph-based algorithms can be heavily dependent upon the input hypergraph sizes. To address the ever-increasing computational challenges, graph coarsening can be potentially applied for preprocessing a given hypergraph by aggressively aggregating its vertices (nodes). However, state-of-the-art hypergraph partitioning (clustering) methods that incorporate heuristic graph coarsening techniques are not optimized for preserving the structural (global) properties of hypergraphs. In this work, we propose an efficient spectral hypergraph coarsening scheme (HyperSF) for well preserving the original spectral (structural) properties of hypergraphs. Our approach leverages a recent strongly-local max-flow-based clustering algorithm for detecting the sets of hypergraph vertices that minimize ratio cut. To further improve the algorithm efficiency, we propose a divide-and-conquer scheme by leveraging spectral clustering of the bipartite graphs corresponding to the original hypergraphs. Our experimental results for a variety of hypergraphs extracted from real-world VLSI design benchmarks show that the proposed hypergraph coarsening algorithm can significantly improve the multi-way conductance of hypergraph clustering as well as runtime efficiency when compared with existing state-of-the-art algorithms.

cs.LG↗

arXiv2021

SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation

A black-box spectral method is introduced for evaluating the adversarial robustness of a given machine learning (ML) model. Our approach, named SPADE, exploits bijective distance mapping between the input/output graphs constructed for approximating the manifolds corresponding to the input/output data. By leveraging the generalized Courant-Fischer theorem, we propose a SPADE score for evaluating the adversarial robustness of a given model, which is proved to be an upper bound of the best Lipschitz constant under the manifold setting. To reveal the most non-robust data samples highly vulnerable to adversarial attacks, we develop a spectral graph embedding procedure leveraging dominant generalized eigenvectors. This embedding step allows assigning each data sample a robustness score that can be further harnessed for more effective adversarial training. Our experiments show the proposed SPADE method leads to promising empirical results for neural network models that are adversarially trained with the MNIST and CIFAR-10 data sets.

cs.LG↗

arXiv2020

SF-GRASS: Solver-Free Graph Spectral Sparsification

Recent spectral graph sparsification techniques have shown promising performance in accelerating many numerical and graph algorithms, such as iterative methods for solving large sparse matrices, spectral partitioning of undirected graphs, vectorless verification of power/thermal grids, representation learning of large graphs, etc. However, prior spectral graph sparsification methods rely on fast Laplacian matrix solvers that are usually challenging to implement in practice. This work, for the first time, introduces a solver-free approach (SF-GRASS) for spectral graph sparsification by leveraging emerging spectral graph coarsening and graph signal processing (GSP) techniques. We introduce a local spectral embedding scheme for efficiently identifying spectrally-critical edges that are key to preserving graph spectral properties, such as the first few Laplacian eigenvalues and eigenvectors. Since the key kernel functions in SF-GRASS can be efficiently implemented using sparse-matrix-vector-multiplications (SpMVs), the proposed spectral approach is simple to implement and inherently parallel friendly. Our extensive experimental results show that the proposed method can produce a hierarchy of high-quality spectral sparsifiers in nearly-linear time for a variety of real-world, large-scale graphs and circuit networks when compared with the prior state-of-the-art spectral method.

cs.DS↗

arXiv2020

GRASPEL: Graph Spectral Learning at Scale

Learning meaningful graphs from data plays important roles in many data mining and machine learning tasks, such as data representation and analysis, dimension reduction, data clustering, and visualization, etc. In this work, for the first time, we present a highly-scalable spectral approach (GRASPEL) for learning large graphs from data. By limiting the precision matrix to be a graph Laplacian, our approach aims to estimate ultra-sparse (tree-like) weighted undirected graphs and shows a clear connection with the prior graphical Lasso method. By interleaving the latest high-performance nearly-linear time spectral methods for graph sparsification, coarsening and embedding, ultra-sparse yet spectrally-robust graphs can be learned by identifying and including the most spectrally-critical edges into the graph. Compared with prior state-of-the-art graph learning approaches, GRASPEL is more scalable and allows substantially improving computing efficiency and solution quality of a variety of data mining and machine learning applications, such as spectral clustering (SC), and t-Distributed Stochastic Neighbor Embedding (t-SNE). {For example, when comparing with graphs constructed using existing methods, GRASPEL achieved the best spectral clustering efficiency and accuracy.

cs.LG↗