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As a typical nonradiative multiphonon transition in semiconductors, carrier capture at defects is critical to the performance of semiconductor devices. Its transition rate is usually calculated using the equal-mode approximation, which assumes that phonon modes and frequencies remain unchanged before and after the transition. Using the carbon substitutional defect ($\text{C}_\text{N}$) in GaN as a benchmark, here we demonstrate that the phonon renormalization can be significant during defect relaxation, which causes errors as large as orders of magnitude in the approximation. To address this issue, we consider (i) Duschinsky matrix connecting the initial-state and final-state phonons, which accounts for the changes in phonon modes and frequencies; and (ii) the off-diagonal contributions in total transition matrix element, which incorporates the cross terms of electron-phonon interactions between different modes. With this improvement, the calculated transition rates show agreements with experimental results within an order of magnitude. We believe the present method makes one step forward for the accurate calculation of multiphonon transition rate, especially in cases with large defect relaxations.

Searching for ambient-pressure conventional superconductors with critical temperatures ($T_C$) higher than 40 K and implementable synthesis routes is a key challenge in the field of high-temperature superconductivity, mainly due to lack of efficient and effective models to estimate $T_C$ of potential systems. In this work, we propose a simplified model to estimate the dimensionless electron-phonon coupling (EPC) strength \lambbda and thus $T_C$ by separately treating the EPC matrix elements which evaluate the pairing strength and the phonon-assisted nesting function P(w) which evaluates the matching of electron bands and phonon spectra for forming potential electron pairs via phonons. By applying the model to screen over the Computational 2D Materials Database (C2DB), we successfully identify several systems with TC exceeding 20 K as confirmed by accurate first-principles calculations. Especially, Ti$_3$N$_2$H$_2$ has a record high $T_C$ value among known MXenes, which is 52 K (78 K) according to isotropic (anisotropic) Migdal-Eliashberg equations under ambient pressure. More importantly, we propose a feasible synthesis route for Ti$_3$N$_2$H$_2$ starting from the experimentally synthesized MXene Ti$_4$N$_3$. Beyond demonstrating its feasibility and efficiency in identifying high-$T_C$ superconductors, our model illuminates the critical roles of the matching between electron band and phonon spectra as a necessary condition in determining $T_C$ and points out the directions for prediction and design of high-$T_C$ superconductors, which is exemplified by showing a novel designed system Ti$_3$N$_2$H$_2$ with a possible $T_C$ of 80 K under ambient pressure. Our model opens new avenues for exploring high-$T_C$ systems.

Discovery of clusters with high symmetrical geometry, such as C60 fullerene, always attract lots of interest because of their diverse nature. However, most of such interesting cluster were sporadically discovered, is there any systematic method to explore all possible high symmetrical clusters? Herein, we propose an idea to systematically construct high symmetrical structures based on novel conjugate-dual (co-dual) concept. A co-dual structure would be constructed by conjugately combining one high symmetrical polyhedron and its corresponding dual. In such co-dual structures, the symmetry of the original polyhedron could be kept or even promoted in some special cases. This provides a way to explore high symmetrical polyhedra and leads to a new cluster family, co-dual clusters. In this paper, we have systematically studied the spherical co-dual clusters with one- or two-element shells, and found a series of stable cage-like and core-shell clusters. The co-dual structures would be a new treasure box for hunting novel clusters.

Intrinsic breakdown strength (F_bd), as the theoretical upper limit of electric field strength that a material can sustain, plays important roles in determining dielectric and safety performance. The well accepted concept is that a larger band gap (E_g) often leads to a larger intrinsic breakdown strength. In this work, we analytically derive a simplified model of F_bd, showing a linear relationship between F_bd and the maximum electron density of states (DOS_max) within the energy range spanning from the conduction band minimum (CBM) to CBM+E_g. Using the Wannier interpolation technique to reduce the cost of calculating the F_bd for various three- and two-dimensional materials, we find that the calculated F_bd did not show any simple relationship with band gap, but it behaves linearly with the DOS_max, consistent with our theoretical derivation. Our work shows that the DOS_max is more fundamental than the band gap value in determining the F_bd, thus providing useful physical insights into the intrinsic dielectric breakdown strength and opening directions for improving high-power devices. The dimensional effects on F_bd has also been revealed that monolayers tend to have larger F_bd due to reduced screening effects.

Learning-based methods for underwater image enhancement (UWIE) have undergone extensive exploration. However, learning-based models are usually vulnerable to adversarial examples so as the UWIE models. To the best of our knowledge, there is no comprehensive study on the adversarial robustness of UWIE models, which indicates that UWIE models are potentially under the threat of adversarial attacks. In this paper, we propose a general adversarial attack protocol. We make a first attempt to conduct adversarial attacks on five well-designed UWIE models on three common underwater image benchmark datasets. Considering the scattering and absorption of light in the underwater environment, there exists a strong correlation between color correction and underwater image enhancement. On the basis of that, we also design two effective UWIE-oriented adversarial attack methods Pixel Attack and Color Shift Attack targeting different color spaces. The results show that five models exhibit varying degrees of vulnerability to adversarial attacks and well-designed small perturbations on degraded images are capable of preventing UWIE models from generating enhanced results. Further, we conduct adversarial training on these models and successfully mitigated the effectiveness of adversarial attacks. In summary, we reveal the adversarial vulnerability of UWIE models and propose a new evaluation dimension of UWIE models.

It is of critical importance to analyse the factors influencing college students' intention to use generative artificial intelligence (GenAI) to understand and predict learners' learning behaviours and academic outcomes. Nevertheless, a lack of congruity has been shown in extant research results. This study, therefore, conducted a meta-analysis of 27 empirical studies under an integrated theoretical framework, including 87 effect sizes of independent research and 33,833 sample data. The results revealed that the main variables are strongly correlated with students' behavioural intention to use GenAI. Among them, performance expectancy (r = 0.389) and attitudes (r = 0.576) play particularly critical roles, and effort expectancy and habit are moderated by locational factors. Gender, notably, only moderated attitudes on students' behavioural intention to use GenAI. This study provides valuable insights for addressing the debate regarding students' intention to use GenAI in existed research, improving educational technology, as well as offering support for school decision-makers and educators to apply GenAI in school settings.

Non-adiabatic molecular dynamics (NAMD) simulations have become an indispensable tool for investigating excited-state dynamics in solids. In this work, we propose a general framework, N$^2$AMD which employs an E(3)-equivariant deep neural Hamiltonian to boost the accuracy and efficiency of NAMD simulations. The preservation of Euclidean symmetry of Hamiltonian enables N$^2$AMD to achieve state-of-the-art performance. Distinct from conventional machine learning methods that predict key quantities in NAMD, N$^2$AMD computes these quantities directly with a deep neural Hamiltonian, ensuring supreme accuracy, efficiency, and consistency. Furthermore, N$^2$AMD demonstrates excellent generalizability and enables seamless integration with advanced NAMD techniques and infrastructures. Taking several extensively investigated semiconductors as the prototypical system, we successfully simulate carrier recombination in both pristine and defective systems at large scales where conventional NAMD often significantly underestimates or even qualitatively incorrectly predicts lifetimes. This framework not only boosts the efficiency and precision of NAMD simulations but also opens new avenues to advance materials research., Comment: 25 pages, 7 figures

Point cloud registration (PCR) involves determining a rigid transformation that aligns one point cloud to another. Despite the plethora of outstanding deep learning (DL)-based registration methods proposed, comprehensive and systematic studies on DL-based PCR techniques are still lacking. In this paper, we present a comprehensive survey and taxonomy of recently proposed PCR methods. Firstly, we conduct a taxonomy of commonly utilized datasets and evaluation metrics. Secondly, we classify the existing research into two main categories: supervised and unsupervised registration, providing insights into the core concepts of various influential PCR models. Finally, we highlight open challenges and potential directions for future research. A curated collection of valuable resources is made available at https://github.com/yxzhang15/PCR., Comment: This paper is accepted by IJCAI 2024

Charge transfer in type-II heterostructures plays important roles in determining device performance for photovoltaic and photocatalytic applications. However, current theoretical studies of charge transfer process don't consider the effects of operating conditions such as illuminations and yield systemically larger interlayer transfer time of hot electrons in MoS2/WS2 compared to experimental results. Here in this work, we propose a general picture that, illumination can induce interfacial dipoles in type-II heterostructures, which can accelerate hot carrier transfer by reducing the energy difference between the electronic states in separate materials and enhancing the nonadiabatic couplings. Using the first-principles calculations and the ab-initio nonadiabatic molecular dynamics, we demonstrate this picture using MoS2/WS2 as a prototype. The calculated characteristic time for the interlayer transfer (60 fs) and the overall relaxation (700 fs) processes of hot electrons is in good agreement with the experiments. We further find that illumination mainly affects the ultrafast interlayer transfer process but has little effects on the relatively slow intralayer relaxation process. Therefore, the overall relaxation process of hot electrons has a saturated time with increased illumination strengths. The illumination-accelerated charge transfer is expected to universally exist in type-II heterostructures.

Illumination has been long known to affect semiconductor defect properties during either growth or operating process. Current theories of studying the illumination effects on defects usually have the assumption of unaffected formation energies of neutral defects as well as defect transition energy levels, and use the quasi-Fermi levels to describe behaviors of excess carriers with conclusions at variance. In this work, we first propose a method to simulate steady illumination conditions, based on which we demonstrate that formation energies of neutral defects and defect transition energy levels are insensitive to illumination. Then, we show that optical and thermal excitation of electrons can be seen equivalent with each other to reach a steady electron distribution in a homogeneous semiconductor. Consequently, the electron distribution can be characterized using just one effective temperature T' and one universal Fermi level E_F' for a homogeneous semiconductor under continuous and steady illuminations, which can be seen as a combination of quasi-equilibrium electron system with T' and a lattice system with T. Using the new concepts, we uncover the universal mechanisms of illumination effects on charged defects by treating the band edge states explicitly in the same footing as the defect states. We find that the formation energies of band edge 'defect' states shift with increased T' of electrons, thus affecting the E_F', changing defect ionic probabilities, and affecting concentrations of charged defects. We apply our theory to study the illumination effects on the doping behaviors in GaN:Mg and CdTe:Sb, obtaining results in accordance with experimental observations. More interesting experimental defect-related phenomena under steady illuminations are expected to be understood from our theory.

In this paper, the problem of seeking optimal distributed resource allocation (DRA) policies on cellular networks in the presence of an unknown malicious adding-edge attacker is investigated. This problem is described as the games of games (GoG) model. Specifically, two subnetwork policymakers constitute a Nash game, while the confrontation between each subnetwork policymaker and the attacker is captured by a Stackelberg game. First, we show that the communication resource allocation of cellular networks based on the Foschini-Miljanic (FM) algorithm can be transformed into a \emph{geometric program} and be efficiently solved via convex optimization. Second, the upper limit of attack magnitude that can be tolerated by the network is calculated by the corresponding theory, and it is proved that the above geometric programming (GP) framework is solvable within the attack bound, that is, there exists a Gestalt Nash equilibrium (GNE) in our GoG. Third, a heuristic algorithm that iteratively uses GP is proposed to identify the optimal policy profiles of both subnetworks, for which asymptotic convergence is also confirmed. Fourth, a greedy heuristic adding-edge strategy is developed for the attacker to determine the set of the most vulnerable edges. Finally, simulation examples illustrate that the proposed theoretical results are robust and can achieve the GNE. It is verified that the transmission gains and interference gains of all channels are well tuned within a limited budget, despite the existence of malicious attacks.

This paper studies the problem of cooperative control of heterogeneous multi-agent systems (MASs) against Byzantine attacks. The agent affected by Byzantine attacks sends different wrong values to all neighbors while applying wrong input signals for itself, which is aggressive and difficult to be defended. Inspired by the concept of Digital Twin, a new hierarchical protocol equipped with a virtual twin layer (TL) is proposed, which decouples the above problems into the defense scheme against Byzantine edge attacks on the TL and the defense scheme against Byzantine node attacks on the cyber-physical layer (CPL). On the TL, we propose a resilient topology reconfiguration strategy by adding a minimum number of key edges to improve network resilience. It is strictly proved that the control strategy is sufficient to achieve asymptotic consensus in finite time with the topology on the TL satisfying strongly $(2f+1)$-robustness. On the CPL, decentralized chattering-free controllers are proposed to guarantee the resilient output consensus for the heterogeneous MASs against Byzantine node attacks. Moreover, the obtained controller shows exponential convergence. The effectiveness and practicality of the theoretical results are verified by numerical examples.

This paper studies the leader-following consensuses of uncertain and nonlinear multi-agent systems against composite attacks (CAs), including Denial of Service (DoS) attacks and actuation attacks (AAs). A double-layer control framework is formulated, where a digital twin layer (TL) is added beside the traditional cyber-physical layer (CPL), inspired by the recent Digital Twin technology. Consequently, the resilient control task against CAs can be divided into two parts: One is distributed estimation against DoS attacks on the TL and the other is resilient decentralized tracking control against actuation attacks on the CPL. %The data-driven scheme is used to deal with both model non-linearity and model uncertainty, in which only the input and output data of the system are employed throughout the whole control process. First, a distributed observer based on switching estimation law against DoS is designed on TL. Second, a distributed model free adaptive control (DMFAC) protocol based on attack compensation against AAs is designed on CPL. Moreover, the uniformly ultimately bounded convergence of consensus error of the proposed double-layer DMFAC algorithm is strictly proved. Finally, the simulation verifies the effectiveness of the resilient double-layer control scheme.

This paper studies the distributed resilient output containment control of heterogeneous multiagent systems against composite attacks, including denial-of-services (DoS) attacks, false-data injection (FDI) attacks, camouflage attacks, and actuation attacks. Inspired by digital twins, a twin layer (TL) with higher security and privacy is used to decouple the above problem into two tasks: defense protocols against DoS attacks on TL and defense protocols against actuation attacks on cyber-physical layer (CPL). First, considering modeling errors of leader dynamics, we introduce distributed observers to reconstruct the leader dynamics for each follower on TL under DoS attacks. Second, distributed estimators are used to estimate follower states according to the reconstructed leader dynamics on the TL. Third, according to the reconstructed leader dynamics, we design decentralized solvers that calculate the output regulator equations on CPL. Fourth, decentralized adaptive attack-resilient control schemes that resist unbounded actuation attacks are provided on CPL. Furthermore, we apply the above control protocols to prove that the followers can achieve uniformly ultimately bounded (UUB) convergence, and the upper bound of the UUB convergence is determined explicitly. Finally, two simulation examples are provided to show the effectiveness of the proposed control protocols.

We introduce a computational method to optimize target physical properties in the full configuration space regarding atomic composition, chemical stoichiometry, and crystal structure. The approach combines the universal potential of the crystal graph neural network and Bayesian optimization. The proposed approach effectively obtains the crystal structure with the strongest atomic cohesion from all possible crystals. Several new crystals with high atomic cohesion are identified and confirmed by density functional theory for thermodynamic and dynamic stability. Our method introduces a novel approach to inverse materials design with additional functional properties for practical applications.

Li$_x$CoO$_2$ based batteries have serious capacity degradation and safety issues when cycling at high-delithiation states but full and consistent mechanisms are still poorly understood. Herein, a global neural network potential (GNNP) is developed to provide direct theoretical understandings by performing long-time and large-size atomic simulations. We propose a self-consistent picture as follows: (i) CoO$_2$ layers are easier to glide with longer distances at more highly delithiated states, resulting in structural transitions and structural inhomogeneity; (ii) at regions between different phases with different Li distributions due to gliding, local strains are induced and accumulate during cycling processes; (3) accumulated strains cause the rupture of Li diffusion channels and result in formation of oxygen dimers during cycling especially when Li has inhomogeneous distributions, leading to capacity degradations and safety issues. We find that large tensile strains combined with inhomogeneous distributions of Li ions play critical roles in the formation processes of blocked Li diffusion channels and the oxygen dimers at high-delithiation states, which could be the fundamental origins of capacity degradations and safety issues. Correspondingly, suppressing accumulations of strains by controlling charge and discharge conditions as well as suppressing the gliding will be helpful for improving the performance of lithium-ion batteries (LIBs)., Comment: 17 pages, 5 figures, 1 table

Despite the successes of machine learning methods in physical sciences, prediction of the Hamiltonian, and thus electronic properties, is still unsatisfactory. Here, based on graph neural network architecture, we present an extendable neural network model to determine the Hamiltonian from ab initio data, with only local atomic structures as inputs. Rotational equivariance of the Hamiltonian is achieved by our complete local coordinates. The local coordinates information, encoded using the convolutional neural network and designed to preserve Hermitian symmetry, is used to map hopping parameters onto local structures. We demonstrate the performance of our model using graphene and SiGe random alloys as examples. We show that our neural network model, although trained using small-size systems, can predict the Hamiltonian, as well as electronic properties such as band structures and densities of states (DOS) for large-size systems within the ab initio accuracy, justifying its extensibility. In combination with the high efficiency of our model, which takes only seconds to get the Hamiltonian of a 1728-atom system, present work provides a general framework to predict electronic properties efficiently and accurately, which provides new insights into computational physics and will accelerate the research for large-scale materials., Comment: 21 pages, 7 figures

Ideal two-dimensional (2D) semiconductors with high mobility comparable to three-dimensional (3D) Si or GaAs are still lacking, hindering the development of high-performance 2D devices. Here in this work, using first-principles calculations and considering all the electron-phonon couplings, we show that monolayer BX (X = P, As, Sb) with honeycomb lattices have intrinsic phonon-limited carrier mobility reaching record-high values of 1200-14000 $cm^2V^{-1}s^{-1}$ at room temperature. Despite being polar and the band edges located at the K point with multiple valleys, these three systems unusually have small carrier scattering rates. Detailed analysis shows that, both the intravalley scattering and the intervalley scattering between two equivalent K points are weak, which can be understood from the large mismatch between the electron bands and phonon spectrum and suppressed electron-phonon coupling strength. Furthermore, we reveal the general trend of mobility increase from BP to BAs and to BSb and conclude that: smaller effective masses, larger sound velocities, higher optical phonon energies, heavy atomic masses, and out-of-plane orbitals tend to result in small match between the electron and phonon bands, small electron-phonon coupling strengths, and thus high mobility. Our work demonstrates that 2D semiconductors can achieve comparable carrier mobility to 3D GaAs, thus opening doors to 2D high-performance electronic devices., Comment: 25 pages, 8 figures