arXiv subjects

Xiuying Zhang

Publications and source records attributed to Xiuying Zhang.

11 recordsLinked to original sources

arXiv2025

A Spatio-Temporal Hybrid Quantum-Classical Graph Convolutional Neural Network Approach for Urban Taxi Destination Prediction

We propose a Hybrid Spatio-Temporal Quantum Graph Convolutional Network (H-STQGCN) algorithm by combining the strengths of quantum computing and classical deep learning to predict the taxi destination within urban road networks. Our algorithm consists of two branches: spatial processing and time evolution. Regarding the spatial processing, the classical module encodes the local topological features of the road network based on the GCN method, and the quantum module is designed to map graph features onto parameterized quantum circuits through a differentiable pooling layer. The time evolution is solved by integrating multi-source contextual information and capturing dynamic trip dependencies on the classical TCN theory. Finally, our experimental results demonstrate that the proposed algorithm outperforms the current methods in terms of prediction accuracy and stability, validating the unique advantages of the quantum-enhanced mechanism in capturing high-dimensional spatial dependencies.

quant-ph↗

arXiv2025

Modeling crystal defects using defect-informed neural networks

Most AI-for-Materials research to date has focused on ideal crystals, whereas real-world materials inevitably contain defects that play a critical role in modern functional technologies. The defects break geometric symmetry and increase interaction complexity, posing particular challenges for traditional ML models. Here, we introduce Defect-Informed Equivariant Graph Neural Network (DefiNet), a model specifically designed to accurately capture defect-related interactions and geometric configurations in point-defect structures. DefiNet achieves near-DFT-level structural predictions in milliseconds using a single GPU. To validate its accuracy, we perform DFT relaxations using DefiNet-predicted structures as initial configurations and measure the residual ionic steps. For most defect structures, regardless of defect complexity or system size, only 3 ionic steps are required to reach the DFT-level ground state. Finally, comparisons with scanning transmission electron microscopy (STEM) images confirm DefiNet's scalability and extrapolation beyond point defects, positioning it as a valuable tool for defect-focused materials research.

cond-mat.mtrl-sci↗

arXiv2025

A low-rank algorithm for solving Lyapunov operator $φ$-functions within the matrix-valued exponential integrators

In this work we present a low-rank algorithm for computing low-rank approximations of large-scale Lyapunov operator $φ$-functions. These computations play a crucial role in implementing of matrix-valued exponential integrators for large-scale stiff matrix differential equations, where the (approximate) solutions are of low rank.The proposed method employs a scaling and recursive procedure, complemented by a quasi-backward error analysis to determine the optimal parameters. The computational cost is primarily determined by the multiplication of sparse matrices with block vectors. Numerical experiments validate the effectiveness of the proposed method as a foundational tool for matrix-valued exponential integrators in solving differential Lyapunov equations and Riccati equations.

math.NA↗

arXiv2024

Twist-induced spin splitting and spin-Hall-like effect in antiferromagnetic bilayers

Momentum-resolved spin-polarized bands are a key ingredient in many proposed spintronic devices, but their existence often relies on lattice commensurability or strong spin-orbit coupling. By a large-scale DFT calculation (up to 4212 atoms), we propose a way to realize strongly spin-polarized bands in the absence of these ingredients by twisting monolayers of van der Waals magnetic semiconductor CrSBr. Furthermore, due to the highly anisotropic electronic transport in this material, the twist-induced electronic transport becomes strongly coupled to the spin transport. We show that an in-plane electric field induces a transverse spin current, manifesting a twist-tunable spin-Hall effect in the absence of spin-orbit coupling. Using highthroughput computations, we also identify 231 other material candidates out of a set of 6000 magnetic two-dimensional materials, which satisfy the necessary conditions to realize this behavior, paving the way to widespread application of twist-tunable spin transport.

cond-mat.mtrl-sci↗

arXiv2024

Scalable Crystal Structure Relaxation Using an Iteration-Free Deep Generative Model with Uncertainty Quantification

In computational molecular and materials science, determining equilibrium structures is the crucial first step for accurate subsequent property calculations. However, the recent discovery of millions of new crystals and complex twisted structures has challenged traditional computational methods, both ab initio and machine-learning-based, due to their computationally intensive iterative processes. To address these scalability issues, here we introduce DeepRelax, a deep generative model capable of performing geometric crystal structure relaxation rapidly and without iterations. DeepRelax learns the equilibrium structural distribution, enabling it to predict relaxed structures directly from their unrelaxed ones. The ability to perform structural relaxation at the millisecond level per structure, combined with the scalability of parallel processing, makes DeepRelax particularly useful for large-scale virtual screening. We demonstrate DeepRelax's reliability and robustness by applying it to five diverse databases, including oxides, Materials Project, two-dimensional materials, van der Waals crystals, and crystals with point defects. DeepRelax consistently shows high accuracy and efficiency, validated by density functional theory calculations. Finally, we enhance its trustworthiness by integrating uncertainty quantification. This work significantly accelerates computational workflows, offering a robust and trustworthy machine-learning method for material discovery and advancing the application of AI for science. Code for DeepRelax is available at https://github.com/Shen-Group/DeepRelax.

cond-mat.mtrl-sci↗

arXiv2024

Physics-integrated Neural Network for Quantum Transport Prediction of Field-effect Transistor

Quantum-mechanics-based transport simulation is of importance for the design of ultra-short channel field-effect transistors (FETs) with its capability of understanding the physical mechanism, while facing the primary challenge of the high computational intensity. Traditional machine learning is expected to accelerate the optimization of FET design, yet its application in this field is limited by the lack of both high-fidelity datasets and the integration of physical knowledge. Here, we introduced a physics-integrated neural network framework to predict the transport curves of sub-5-nm gate-all-around (GAA) FETs using an in-house developed high-fidelity database. The transport curves in the database are collected from literature and our first-principles calculations. Beyond silicon, we included indium arsenide, indium phosphide, and selenium nanowires with different structural phases as the FET channel materials. Then, we built a physical-knowledge-integrated hyper vector neural network (PHVNN), in which five new physical features were added into the inputs for prediction transport characteristics, achieving a sufficiently low mean absolute error of 0.39. In particular, ~98% of the current prediction residuals are within one order of magnitude. Using PHVNN, we efficiently screened out the symmetric p-type GAA FETs that possess the same figures of merit with the n-type ones, which are crucial for the fabrication of homogeneous CMOS circuits. Finally, our automatic differentiation analysis provides interpretable insights into the PHVNN, which highlights the important contributions of our new input parameters and improves the reliability of PHVNN. Our approach provides an effective method for rapidly screening appropriate GAA FETs with the prospect of accelerating the design process of next-generation electronic devices.

cond-mat.dis-nn↗

arXiv2024

Dynamical Coulomb Blockade as a Signature of the Sign-Reversing Cooper Pairing Potential

Coulomb blockade occurs for electrons tunneling into nanoislands because of the quantization of charge. Here, using spectroscopy measurements of nonmagnetic islands grown on a high-Tc superconductor [one-unit-cell (1-UC) FeSe], we systematically investigate the dynamical Coulomb blockade (DCB), which is found to reflect the Cooper pairing potential in the superconducting substrate. The tunneling spectra are acquired on single-crystalline Pb nanoislands and show a clear suppression of the tunnel current around zero bias-voltage with a gap-like structure. The observed spectral gaps can be attributed to DCB based on our comprehensive investigations, including experiments with finely varying island sizes and calculations of the spectra using the P(E) theory of DCB. Our detailed analysis suggests that the observed DCB can be related to the sign-reversing pairing potential in the 1-UC FeSe substrate below the islands. The sign reversal is furthermore revealed in a transition of the superconducting gap of FeSe from a U- to a V-like lineshape as the distance between neighboring doublet islands is decreased, indicating the presence of a nodal-like gap as expected for a sign-reversing superconductor. Our configuration of nonmagnetic nanoislands on a high-Tc superconductor for spectroscopy measurements may serve as a local, spatially sensitive, and tunable probe for detecting the sign-reversing order parameter in unconventional superconductors.

cond-mat.supr-con↗

arXiv2024

Efficient algorithm for the oscillatory matrix functions

This paper introduces an efficient algorithm for computing the general oscillatory matrix functions. These computations are crucial for solving second-order semi-linear initial value problems. The method is exploited using the scaling and restoring technique based on a quadruple angle formula in conjunction with a truncated Taylor series. The choice of the scaling parameter and the degree of the Taylor polynomial relies on a forward error analysis. Numerical experiments show that the new algorithm behaves in a stable fashion and performs well in both accuracy and efficiency.

math.NA↗

arXiv2023

Local environment-based machine learning for molecular adsorption energy prediction

Most machine learning (ML) models in Materials Science are developed by global geometric features, often falling short in describing localized characteristics, like molecular adsorption on materials. In this study, we introduce a local environment framework that extracts local features from crystal structures to portray the environment surrounding specific adsorption sites. Upon OC20 database (~20,000 3D entries), we apply our local environment framework on several ML models, such as random forest, convolutional neural network, and graph neural network. It is found that our framework achieves remarkable prediction accuracy in predicting molecular adsorption energy, significantly outperforming other examined global-environment-based models. Moreover, the employment of this framework reduces data requirements and augments computational speed, specifically for deep learning algorithms. Finally, we directly apply our Local Environment ResNet (LERN) on a small 2DMatPedia database (~2,000 2D entries), which also achieves highly accurate prediction, demonstrating the model transferability and remarkable data efficiency. Overall, the prediction accuracy, data-utilization efficiency, and transferability of our local-environment-based ML framework hold a promising high applicability across a broad molecular adsorption field, such as catalysis and sensor technologies.

cond-mat.mtrl-sci↗

arXiv2022

Computing the Lyapunov operator φ-functions, with an application to matrix-valued exponential integrators

In this paper, we develop efficient and accurate evaluation for the Lyapunov operator function $φ_l(\mathcal{L}_A)[Q],$ where $φ_l(\cdot)$ is the function related to the exponential, $\mathcal{L}_A$ is a Lyapunov operator and $Q$ is a symmetric and full-rank matrix. An important application of the algorithm is to the matrix-valued exponential integrators for matrix differential equations such as differential Lyapunov equations and differential Riccati equations. The method is exploited by using the modified scaling and squaring procedure combined with the truncated Taylor series. A quasi-backward error analysis is presented to determine the value of the scaling parameter and the degree of the Taylor approximation. Numerical experiments show that the algorithm performs well in both accuracy and efficiency.

math.NA↗

arXiv2022

Interpretable learning of voltage for electrode design of multivalent metal-ion batteries

Deep learning (DL) has indeed emerged as a powerful tool for rapidly and accurately predicting materials properties from big data, such as the design of current commercial Li-ion batteries. However, its practical utility for multivalent metal-ion batteries (MIBs), the most promising future solution of large-scale energy storage, is limited due to the scarce MIB data availability and poor DL model interpretability. Here, we develop an interpretable DL model as an effective and accurate method for learning electrode voltages of multivalent MIBs (divalent magnesium, calcium, zinc, and trivalent aluminum) at small dataset limits (150~500). Using the experimental results as validation, our model is much more accurate than machine-learning models which usually are better than DL in the small dataset regime. Besides the high accuracy, our feature-engineering-free DL model is explainable, which automatically extracts the atom covalent radius as the most important feature for the voltage learning by visualizing vectors from the layers of the neural network. The presented model potentially accelerates the design and optimization of multivalent MIB materials with fewer data and less domain-knowledge restriction, and is implemented into a publicly available online tool kit in http://batteries.2dmatpedia.org/ for the battery community.

cond-mat.mtrl-sci↗