arXiv subjects

Xinghua Shi

Publications and source records attributed to Xinghua Shi.

12 recordsLinked to original sources

arXiv2026

A Unified Graph Neural Network Framework for Non-Equilibrium Carrier and Lattice Dynamics Driven by Electric Fields

Finite-temperature simulations of electric-field-driven dynamics need a unified description of interatomic interactions, local electronic states, and configuration-dependent electric responses. First-principles simulations remain scale-limited, whereas conventional machine-learning potentials lack electric-field effects. Recent machine-learning frameworks have incorporated electric-field response or atom-resolved electronic-state information, but rarely both within a single framework. Here, we develop an electric-field-response graph neural network (EFR-GNN) that predicts energies, forces, Born effective charge tensors, atom-resolved charges and magnetic moments, and supports long-time field-driven molecular dynamics with atom-resolved tracking of localized electronic states. In hole-doped MgO, static fields rectify thermally activated hole-polaron hopping through a forward--backward asymmetry quantified by a nearest-neighbor model. In GaAs, resonant terahertz excitation generates a coherent $\Gamma$-point transverse-optical phonon with dephasing consistent with experiment, while opposite helicities reverse its rotation. In superionic $\alpha$-AgI, it reproduces temperature-dependent Ag$^+$ mobility and collective field-driven ionic drift. Together, EFR-GNN offers an approach to finite-temperature simulations of field-driven atomic and localized-carrier dynamics.

physics.comp-ph↗

arXiv2026

HyperEvoGen: Exploring deep phylogeny using non-Euclidean variational inference

Homologous proteins evolve from a common ancestral sequence, constrained by intricate patterns of co-evolving residues. Accurate reconstruction of evolutionary histories remains a challenge, primarily due to the inability of the existing approaches to capture long-range coevolutionary ties and lack of a precise metric to represent the evolutionary distance between sequences. Standard approaches are based on p-distance or substitution-corrected measures such as Jukes-Cantor. These methods saturate in cases of deep evolutionary divergence, losing all evolutionary signal after enough time. We present HyperEvoGen, a Poincaré variational autoencoder with adversarial training, hyperbolic latent geometry, and a compound loss function that learns evolutionarily meaningful representations from single-family alignments. The arrangement of protein sequences in HyperEvoGen's hyperbolic embedding aims to preserve phylogenetic structure and produce latent distances which scale with true evolutionary divergence. HyperEvoGen enables fast, scalable modeling of protein evolution while preserving hierarchical relatedness in a geometry-aware representation. On Potts-coupled simulation benchmarks, it produces more accurate ancestral reconstructions than conventional baselines, and offers higher-quality sequence generation with less training time than Potts models. This combination of accuracy and throughput supports large-family evolutionary studies and accelerates design-oriented applications.

q-bio.QM↗

arXiv2026

Domain-Wall-Mediated Ultralow-Barrier Sliding and Pinning in Ferroelectric Moiré Superlattices Revealed by Machine Learning

Sliding ferroelectrics built from stacked nonpolar monolayers enable out-of-plane polarization and unconventional switching via interlayer sliding, yet the microscopic sliding dynamics remain unclear. Using machine-learning molecular dynamics, we reveal spontaneous thermally driven interlayer sliding in ferroelectric MoS2 moiré superlattices, with relative velocities on the order of 1 m/s at 300 K. Instead of rigid translation of the entire bilayer, the motion appears as a global drift of the moiré pattern. Such thermally driven sliding is inconsistent with the meV/atom-scale rigid-sliding barrier. In contrast, when constrained relaxation is allowed, the sliding proceeds along an almost barrierless pathway that directly reproduces the global drift of the moiré pattern. Furthermore, sulfur vacancies trigger a sliding-to-pinning transition, with about 0.1% S vacancies already sufficient to convert the long-range sliding into localized oscillations. Notably, these phenomena are not restricted to small twist angles, but arise generically in twisting-induced multidomain structures. These results reveal that the sliding process is governed by a domain-wall-mediated collective reconstruction pathway with an ultralow barrier, rather than rigid layer translation, deepening the understanding of microscopic dynamics in moiré superlattices and sliding ferroelectrics.

cond-mat.mtrl-sci↗

arXiv2025

Anisotropic Photostriction and Strain-modulated Carrier Lifetimes in Orthorhombic Semiconductors

We demonstrate anisotropic photostriction in two-dimensional orthorhombic semiconductors using time-dependent density functional theory. By tracing the dynamics of photoexcited carriers, we establish a quantitative link between carrier density and lattice deformation in layered black phosphorus and germanium selenides. The structural response exhibits significant anisotropy, featuring lattice expansion along the armchair direction and contraction along the zigzag direction, which is attributed to the interplay between charge redistribution and intrinsic lattice anisotropy. Both the magnitude and orientation of the photostrictive strains can be tuned by photodoping densities, enabling precise control over the photoinduced response. Notably, the photoinduced strains significantly increase carrier recombination lifetimes by suppressing nonradiative recombination, primarily due to the enlarged bandgap and weakened nonadiabatic coupling. These results provide microscopic insight into the origin of anisotropic photostriction in low-dimensional systems and lay the groundwork for light-controllable, directionally sensitive optomechanical devices at the atomic scale.

cond-mat.mtrl-sci↗

arXiv2025

Generative Modeling of Entangled Polymers with a Distance-Based Variational Autoencoder

We present a variational autoencoder framework for learning and generating configurations of structured polymer globules from distance matrices. We used coarse-grained molecular dynamics to sample polyethylene structures, which we used as the training set for our deep learning model. By combining convolution and attention layers, the model encodes the structural patterns of distance matrices into an organized and roto-translationally invariant latent space of lower dimensionality. The generative capability of the variational autoencoder, coupled with a post-processing pipeline based on multidimensional scaling and short molecular dynamics, enables the recovery of physically meaningful configurations. The reconstructed and generated samples reproduce key observables, including energy, size, and entanglement, despite minor discrepancies in the raw decoder output.

physics.comp-ph↗

arXiv2025

Photostriction Facilitates Relaxation of Lattice Distortion in Two-Dimensional Perovskites

The photostriction effect, a light-induced mechanical deformation in materials, originates from the intricate interplay between lattice structure and electronic excitation. In photovoltaic semiconductors, this effect plays a crucial role in shaping non-equilibrium structural responses, yet its fundamental mechanism remains elusive. Here, we uncover lattice expansion and structural reconfiguration in two-dimensional (2D) perovskites driven by photoinduced excitation using first-principles calculations. Our findings reveal that the photoinduced carriers lead to a substantial lattice expansion by about 2%. The expanded lattice facilitates strain relaxation with the amplitude of 20% by increasing interatomic distances and reducing internal stresses, thereby enhancing structural stability. The lattice dynamics can be systematically engineered through photodoping density, unveiling a new pathway to modulate light-matter interactions in 2D perovskites. These insights not only advance the understanding of optically driven structural dynamics but also offer a guiding principle for optimizing next-generation high-efficiency photovoltaic devices and optoelectronics.

cond-mat.mtrl-sci↗

arXiv2025

Photoexcitation-induced Stacking Transition Assisted by Intralayer Reconstruction in Charge-Density-Wave Materials

Laser excitation has emerged as an effective tool for probing microscopic interactions and manipulating phases of matter. Among charge density wave (CDW) materials, 1T-TaS2 has garnered significant attention due to its diverse stacking orders and photoexcited responses. However, the mechanisms driving transitions among different stacking orders and the microscopic out-of-equilibrium dynamics remain unclear. We elucidate that photoexcitation can introduce interlayer stacking order transitions facilitated by laser-induced intralayer reconstruction in 1T-TaS2. Importantly, our finding reveals a novel pathway to introduce different phases through laser excitations, apparently distinct from thermally-induced phase transitions via interlayer sliding. In particular, photoexcitation is able to considerably change potential energy surfaces and evoke collective lattice dynamics. Consequently, the laser-induced intralayer reconstruction plays a crucial role in interlayer stacking-order transition, offering a new method to create exotic stackings and quantum phases. The exploration opens up great opportunities for manipulating CDW phases and electronic properties on the femtosecond timescale.

cond-mat.mtrl-sci↗

arXiv2024

Ultrafast Polarization Switching via Laser-activated Ionic Migration in Ferroelectric CuInP$_2$S$_6$

As a layered ferroelectric material, CuInP2S6 has garnered significant attention for its robust ferroelectric state and potential applications in memory devices. In this work, we demonstrate that with short laser pulses ultrafast reversible polarization switching within hundreds of femtoseconds can be achieved in ferroelectric CuInP$_2$S$_6$. Specifically, photoexcitation triggers collective ionic migration and ferroelectricity reversal in CuInP$_2$S$_6$, revealing a novel pathway to access different ferroelectric phases through optical excitation. Our findings indicate that laser pulses substantially alter the transition barriers, promoting ionic transport facilitated by the photodoping effect. This laser-induced ionic migration proves critical for enabling polarization transitions, offering a novel pathway to explore and control exotic quantum phases. These insights open exciting possibilities for manipulating ferroelectric states and electronic properties on an unprecedented ultrafast timescale.

cond-mat.mtrl-sci↗

arXiv2023

Deciphering Non-Gaussianity of Diffusion Based on the Evolution of Diffusivity

Non-Gaussianity indicates complex dynamics related to extreme events or significant outliers. However, the correlation between non-Gaussianity and the dynamics of heterogeneous environments in anomalous diffusion remains uncertain. Inspired by a recent study by Alexandre et al. [Phys. Rev. Lett. 130, 077101 (2023)], we demonstrate that non-Gaussianity can be deciphered through the spatiotemporal evolution of heterogeneity-dependent diffusivity. Using diffusion experiments in a linear temperature field and Brownian dynamics simulations, we found that short- and long-time non-Gaussianity can be predicted based on diffusivity distribution. Non-Gaussianity variation is determined by an effective Péclet number (a ratio of the varying rate of diffusivity to the diffusivity of diffusivity), which clarifies whether the tail distribution expands or contracts. The tail is more Gaussian than exponential over long times, with exceptions significantly dependent on the diffusivity distribution. Our findings shed light on heterogeneity mapping in complex environments using non-Gaussian statistics.

cond-mat.soft↗

arXiv2023

Can Decentralized Stochastic Minimax Optimization Algorithms Converge Linearly for Finite-Sum Nonconvex-Nonconcave Problems?

Decentralized minimax optimization has been actively studied in the past few years due to its application in a wide range of machine learning models. However, the current theoretical understanding of its convergence rate is far from satisfactory since existing works only focus on the nonconvex-strongly-concave problem. This motivates us to study decentralized minimax optimization algorithms for the nonconvex-nonconcave problem. To this end, we develop two novel decentralized stochastic variance-reduced gradient descent ascent algorithms for the finite-sum nonconvex-nonconcave problem that satisfies the Polyak-Łojasiewicz (PL) condition. In particular, our theoretical analyses demonstrate how to conduct local updates and perform communication to achieve the linear convergence rate. To the best of our knowledge, this is the first work achieving linear convergence rates for decentralized nonconvex-nonconcave problems. Finally, we verify the performance of our algorithms on both synthetic and real-world datasets. The experimental results confirm the efficacy of our algorithms.

cs.LG↗

arXiv2016

Nonparametric Detection of Anomalous Data Streams

A nonparametric anomalous hypothesis testing problem is investigated, in which there are totally n sequences with s anomalous sequences to be detected. Each typical sequence contains m independent and identically distributed (i.i.d.) samples drawn from a distribution p, whereas each anomalous sequence contains m i.i.d. samples drawn from a distribution q that is distinct from p. The distributions p and q are assumed to be unknown in advance. Distribution-free tests are constructed using maximum mean discrepancy as the metric, which is based on mean embeddings of distributions into a reproducing kernel Hilbert space. The probability of error is bounded as a function of the sample size m, the number s of anomalous sequences and the number n of sequences. It is then shown that with s known, the constructed test is exponentially consistent if m is greater than a constant factor of log n, for any p and q, whereas with s unknown, m should has an order strictly greater than log n. Furthermore, it is shown that no test can be consistent for arbitrary p and q if m is less than a constant factor of log n, thus the order-level optimality of the proposed test is established. Numerical results are provided to demonstrate that our tests outperform (or perform as well as) the tests based on other competitive approaches under various cases.

cs.LG↗

arXiv2013

Tunable Band Structures of Polycrystalline Graphene by External and Mismatch Strains

Lacking a band gap largely limits the application of graphene in electronic devices. Previous study shows that grain boundaries (GBs) in polycrystalline graphene can dramatically alter the electrical properties of graphene. Here, we investigate the band structure of polycrystalline graphene tuned by externally imposed strains and intrinsic mismatch strains at the GB by density functional theory (DFT) calculations. We found that graphene with symmetrical GBs typically has zero band gap even with large uniaxial and biaxial strain. However, some particular asymmetrical GBs can open a band gap in graphene and their band structures can be substantially tuned by external strains. A maximum band gap about 0.19 eV was observed in matched-armchair GB (5, 5) | (3, 7) with a misorientation of θ=13o when the applied uniaxial strain increases to 9%. Although mismatch strain is inevitable in asymmetrical GBs, it has a small influence on the band gap of polycrystalline graphene.

cond-mat.mtrl-sci↗