arXiv subjects

Xiaoming Wang

Publications and source records attributed to Xiaoming Wang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Photoinduced metastable cation disorder in metal halide double perovskites

Lead-free perovskites have emerged as environmentally benign alternatives to lead-halide counterparts for optoelectronics. Among them, the double perovskite Cs2AgInCl6 family exhibits remarkable white-light emission with proper composition engineering, enabled by strong electron-phonon coupling and the formation of self-trapped excitons (STEs). Despite these advantages, the fundamental photo- and structural dynamics governing their excited-state behavior remain poorly understood. Here, we report a long-lived metastable phase in the Cs2AgInCl6 double perovskite family and unravel this process and the concomitant electronic and structural evolution using a suite of tools including transient optical spectroscopy, time-resolved X-ray diffraction (TR-XRD) and X-ray absorption (TR-XAS). We show that the photoinduced, transient metastable phase is associated with B-site (Ag-In) disorder, which induces a dramatically reduced optical bandgap. Supported by TR-XRD and first-principles calculations, the Ag-In disorder drives the formation of Ag-rich and In-rich domains with millisecond lifetimes, with lifetimes increasing at lower temperatures. TR-XAS further reveals that photogenerated STEs oxidize Ag+ to Ag2+, facilitating this highly temporally asymmetric order-disorder transition. Our findings demonstrate a new mechanism, mediated by hole-localized STE formation, that enables prolongation of transient light-induced states to the multi-millisecond regime in double perovskites, opening possibilities to harvesting the functional properties of metastable phases of these materials.

cond-mat.mtrl-sci↗

arXiv2025

Convergence of long-time stable variable-step arbitrary order ETD-MS scheme for gradient flows with Lipschitz nonlinearity

We analyze a variable-step extension of a family of arbitrarily high-order exponential time differencing multistep (ETD-MS) schemes recently developed by the authors. We prove that the schemes are unconditionally stable in the sense that a modified energy-representing a slight perturbation of the original energy-decreases monotonically over time, provided the nonlinearity is Lipschitz continuous in some appropriate sense. Moreover, we establish optimal-order convergence under mild conditions on the time-step size and local time-step ratio. Numerical experiments on the thin film epitaxial growth model without slope selection, employing a novel variable-step second-order scheme, validate the theoretical findings as well as its potential in developing highly efficient time-adaptive solution.

math.NA↗

arXiv2025

Vanishing layer thickness limit of convection in multilayer porous media

Within the Darcy-Boussinesq framework for convection in multilayered porous media, we investigate the singular limit in which the thickness of one layer tends to zero. We establish that the solution of the full system converges to that of the corresponding limiting model with one fewer layer. The convergence is established in two complementary senses: (i) strong $L^{2}$-convergence over arbitrary finite time intervals, and (ii) upper semi-continuity of the global attractors describing the large-time asymptotic behavior.

math.AP↗

arXiv2025

Neural Network-Guided Symbolic Regression for Interpretable Descriptor Discovery in Perovskite Catalysts

Understanding and predicting the activity of oxide perovskite catalysts for the oxygen evolution reaction (OER) requires descriptors that are both accurate and physically interpretable. While symbolic regression (SR) offers a path to discover such formulas, its performance degrades with high-dimensional inputs and small datasets. We present a two-phase framework that combines neural networks (NN), feature importance analysis, and symbolic regression (SR) to discover interpretable descriptors for OER activity in oxide perovskites. In Phase I, using a small dataset and seven structural features, we reproduce and improve the known μ/t descriptor by engineering composite features and applying symbolic regression, achieving training and validation MAEs of 22.8 and 20.8 meV, respectively. In Phase II, we expand to 164 features, reduce dimensionality, and identify LUMO energy as a key electronic descriptor. A final formula using μ/t, μ/RA, and LUMO energy achieves improved accuracy (training and validation MAEs of 22.1 and 20.6 meV) with strong physical interpretability. Our results demonstrate that NN-guided symbolic regression enables accurate, interpretable, and physically meaningful descriptor discovery in data-scarce regimes, indicating interpretability need not sacrifice accuracy for materials informatics.

physics.data-an↗

arXiv2025

Sharp Material Interface Limit of the Darcy-Boussinesq System

We investigate the sharp material interface limit of the Darcy-Boussinesq model for convection in layered porous media with diffused material interfaces, which allow a gradual transition of material parameters between different layers. We demonstrate that as the thickness of these transition layers approaches zero, the conventional sharp interface model with interfacial boundary conditions, commonly adopted by the fluids community, is recovered under the assumption of constant porosity. Our results validate the widely used sharp interface model by bridging it with the more physically realistic case of diffused material interfaces. This limiting process is singular and involves a boundary layer in the velocity field. Our analysis requires del

math.AP↗

arXiv2025

An efficient scheme for approximating long-time dynamics of a class of non-linear models

We propose a novel, highly efficient, mean-reverting-SAV-BDF2-based, long-time unconditionally stable numerical scheme for a class of finite-dimensional nonlinear models important in geophysical fluid dynamics. The scheme is highly efficient in that only a fixed symmetric positive definite linear problem (with varying right-hand sides) is solved at each time step. The solutions remain uniformly bounded for all time. We show that the scheme accurately captures the long-time dynamics of the underlying geophysical model, with the global attractors and invariant measures of the scheme converging to those of the original model as the step size approaches zero. In our numerical experiments, we adopt an indirect approach, using statistics from long-time simulations to approximate the invariant measures. Our results suggest that the convergence rate of the long-term statistics, as a function of terminal time, is approximately first-order under the Jensen-Shannon metric and half-order under the total variation metric. This implies that extremely long simulations are required to achieve high-precision approximations of the invariant measure (or climate). Nevertheless, the second-order scheme significantly outperforms its first-order counterpart, requiring far less time to reach a small neighborhood of statistical equilibrium for a given step size.

math.NA↗

arXiv2024

Strategically Acting on Information

In many non-cooperative settings, agents often possess useful information that provide an advantage over their opponent(s), but acting on such information too frequently can lead to detection. I develop a simple framework to analyze such a trade-off and characterize the optimal way in which to act on information.

econ.GN↗

arXiv2024

Well-posedness and regularity of the Darcy-Boussinesq system in layered porous media

We establish the existence of global weak solution in 2D and 3D, as well as the uniqueness of weak solution in 2D, for the Darcy-Boussinesq model for convection in layered porous media with square integrable initial data. We also derived tangential regularity in the 2D case. In addition, we obtain the existence and uniqueness of regular solution in a novel piecewise $H^2$ space in both 2D and 3D under uniform porosity assumption and $H^1$ initial data. This is the first rigorous result for this model in the physically important layered setting.

math.AP↗

arXiv2024

Long-time stable SAV-BDF2 numerical schemes for the forced Navier-Stokes equations

We propose a novel second-order accurate, long-time unconditionally stable time-marching scheme for the forced Navier-Stokes equations. A new Forced Scalar Auxiliary Variable approach (FSAV) is introduced to preserve the underlying dissipative structure of the forced system that yields a uniform-in-time estimate of the numerical solution. In addition, the numerical scheme is autonomous if the underlying model is, laying the foundation for studying long-time dynamics of the numerical solution via dynamical system approach. As an example we apply the new algorithm to the two-dimensional incompressible Navier-Stokes equations. In the case with no-penetration and free-slip boundary condition on a simply connected domain, we are also able to derive a uniform-in-time estimate of the vorticity in $H^1$ norm in addition to the $L^2$ norm guaranteed by the general framework. Numerical results demonstrate superior performance of the new algorithm in terms of accuracy, efficiency, stability and robustness.

math.NA↗

arXiv2024

MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains

Recent advances in large language models (LLMs) have increased the demand for comprehensive benchmarks to evaluate their capabilities as human-like agents. Existing benchmarks, while useful, often focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that drive these outcomes. This lack of granularity makes it difficult to deeply discern where failures stem from. Additionally, setting up these environments requires considerable effort, and issues of unreliability and reproducibility sometimes arise, especially in interactive tasks. To address these limitations, we introduce the Massive Multitask Agent Understanding (MMAU) benchmark, featuring comprehensive offline tasks that eliminate the need for complex environment setups. It evaluates models across five domains, including Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming and Mathematics, and covers five essential capabilities: Understanding, Reasoning, Planning, Problem-solving, and Self-correction. With a total of 20 meticulously designed tasks encompassing over 3K distinct prompts, MMAU provides a comprehensive framework for evaluating the strengths and limitations of LLM agents. By testing 18 representative models on MMAU, we provide deep and insightful analyses. Ultimately, MMAU not only sheds light on the capabilities and limitations of LLM agents but also enhances the interpretability of their performance. Datasets and evaluation scripts of MMAU are released at https://github.com/apple/axlearn/tree/main/docs/research/mmau.

cs.AI↗

arXiv2024

A note on the stability of two families of two-step schemes

We investigate the stability of two families of three-level two-step schemes that extend the classical second order BDF (BDF2) and second order Adams-Moulton (AM2) schemes. For a free parameter restricted to an appropriate range that covers the classical case, we show that both the generalized BDF2 and the generalized AM2 schemes are A-stable. We also introduce the concept of uniform-in-time stability which characterizes a scheme's ability to inherit the uniform boundedness over all time of the solution of damped and forced equation with the force uniformly bounded in time. We then demonstrate that A-stability and uniform-in-time stability are equivalent for three-level two-step schemes. Next, these two families of schemes are utilized to construct efficient and unconditionally stable IMEX schemes for systems that involve a damping term, a skew symmetric term, and a forcing term. These novel IMEX schemes are shown to be uniform-in-time energy stable in the sense that the norm of any numerical solution is bounded uniformly over all time, provided that the forcing term is uniformly bounded time, the skew symmetric term is dominated by the dissipative term, together with a mild time-step restriction. Numerical experiments verify our theoretical results. They also indicate that the generalized schemes could be more accurate and/or more stable than the classical ones for suitable choice of the parameter.

math.NA↗

arXiv2024

Convergence analysis of a second order numerical scheme for the Flory-Huggins-Cahn-Hilliard-Navier-Stokes system

We present an optimal rate convergence analysis for a second order accurate in time, fully discrete finite difference scheme for the Cahn-Hilliard-Navier-Stokes (CHNS) system, combined with logarithmic Flory-Huggins energy potential. The numerical scheme has been recently proposed, and the positivity-preserving property of the logarithmic arguments, as well as the total energy stability, have been theoretically justified. In this paper, we rigorously prove second order convergence of the proposed numerical scheme, in both time and space. Since the CHNS is a coupled system, the standard $\ell^\infty (0, T; \ell^2) \cap \ell^2 (0, T; H_h^2)$ error estimate could not be easily derived, due to the lack of regularity to control the numerical error associated with the coupled terms. Instead, the $\ell^\infty (0, T; H_h^1) \cap \ell^2 (0, T; H_h^3)$ error analysis for the phase variable and the $\ell^\infty (0, T; \ell^2)$ analysis for the velocity vector, which shares the same regularity as the energy estimate, is more suitable to pass through the nonlinear analysis for the error terms associated with the coupled physical process. Furthermore, the highly nonlinear and singular nature of the logarithmic error terms makes the convergence analysis even more challenging, since a uniform distance between the numerical solution and the singular limit values of is needed for the associated error estimate. Many highly non-standard estimates, such as a higher order asymptotic expansion of the numerical solution (up to the third order accuracy in time and fourth order in space), combined with a rough error estimate (to establish the maximum norm bound for the phase variable), as well as a refined error estimate, have to be carried out to conclude the desired convergence result.

math.NA↗

arXiv2024

Maximum bound principle and original energy dissipation of arbitrarily high-order rescaled exponential time differencing Runge-Kutta schemes for Allen--Cahn equations

The energy dissipation law and the maximum bound principle are two critical physical properties of the Allen--Cahn equations. While many existing time-stepping methods are known to preserve the energy dissipation law, most apply to a modified form of energy. In this work, we demonstrate that, when the nonlinear term of the Allen--Cahn equation is Lipschitz continuous, a class of arbitrarily high-order exponential time differencing Runge--Kutta (ETDRK) schemes preserve the original energy dissipation property, under a mild step-size constraint. Additionally, we guarantee the Lipschitz condition on the nonlinear term by applying a rescaling post-processing technique, which ensures that the numerical solution unconditionally satisfies the maximum bound principle. Consequently, our proposed schemes maintain both the original energy dissipation law and the maximum bound principle and can achieve arbitrarily high-order accuracy. We also establish an optimal error estimate for the proposed schemes. Some numerical experiments are carried out to verify our theoretical results.

math.NA↗

arXiv2024

Chiral electrons and spin selectivity at chiral-achiral interfaces

Chiral molecules can selectively transport electrons of a particular spin orientation, yet the underlying mechanism remain poorly understood. Here, we present theoretical evidence that electrons propagating through chiral materials with screw symmetry exhibit chirality themselves and carry symmetry-induced pseudo-angular momentum (PAM) which must be characterized by both spin and orbital components. The chiral electron transport is PAM polarized, with counterpropagating states exhibiting opposite PAM. At chiral-achiral interfaces, the PAM polarization is converted to spin polarization due to wave function matching, thereby establishing a novel principle for the chiral-induced spin selectivity.

cond-mat.mtrl-sci↗

arXiv2024

Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge

Rib fractures are a common and potentially severe injury that can be challenging and labor-intensive to detect in CT scans. While there have been efforts to address this field, the lack of large-scale annotated datasets and evaluation benchmarks has hindered the development and validation of deep learning algorithms. To address this issue, the RibFrac Challenge was introduced, providing a benchmark dataset of over 5,000 rib fractures from 660 CT scans, with voxel-level instance mask annotations and diagnosis labels for four clinical categories (buckle, nondisplaced, displaced, or segmental). The challenge includes two tracks: a detection (instance segmentation) track evaluated by an FROC-style metric and a classification track evaluated by an F1-style metric. During the MICCAI 2020 challenge period, 243 results were evaluated, and seven teams were invited to participate in the challenge summary. The analysis revealed that several top rib fracture detection solutions achieved performance comparable or even better than human experts. Nevertheless, the current rib fracture classification solutions are hardly clinically applicable, which can be an interesting area in the future. As an active benchmark and research resource, the data and online evaluation of the RibFrac Challenge are available at the challenge website. As an independent contribution, we have also extended our previous internal baseline by incorporating recent advancements in large-scale pretrained networks and point-based rib segmentation techniques. The resulting FracNet+ demonstrates competitive performance in rib fracture detection, which lays a foundation for further research and development in AI-assisted rib fracture detection and diagnosis.

eess.IV↗

arXiv2024

Near-infrared and Mid-infrared Light Emission of Boron-doped Crystalline Silicon

The bottleneck in achieving fully integrated silicon photonics lies in silicon-based light-emitting devices that are compatible with standard CMOS technology. Dislocation loops by implanting boron into silicon and annealing represents an enticing strategy to transform highly inefficient silicon into a luminescent material. However, the emission at telecommunication wavelength suffers from the strong thermal quenching effect, resulting in low efficiency at room temperature. Here, we applied a new deep cooling process to address this issue. Interestingly, we find that electrons and holes recombine through defects emitting two photons, one in near infrared (NIR, 1.3~1.6 μm) and the other in mid-infrared band (MIR, around 3.5 μm). The PL intensity at NIR increases by three folds when the temperature increases from 77 K to 300K. Furthermore, the NIR light emission of reverse biased silicon diodes was significantly enhanced compared to forward bias, emitting the maximum output power of 42 nW at 60 mA. The results offer new opportunities for the development of IR light sources in integrated circuits.

physics.optics↗

arXiv2024

Analytical impact excitation of Er/O/B co-doped Si light emitting diodes

Er doped Si light emitting diodes may find important applications in the generation and storage of quantum information. These diodes exhibit an emission efficiency two orders of magnitude higher at reverse bias than forward bias due to impact excitation. However, physics of impact excitation in these devices remains largely unexplored. In this work, we fabricated an Er/O/B co-doped Si light emitting diode which exhibits a strong electro-luminescence by the impact excitation of electrons inelastically colliding the Er ions. An analytical impact excitation theory was established to predict the electroluminescence intensity and internal quantum efficiency which fit well with the experimental data. From the fittings, we find that the excitable Er ions reach a record concentration of 1.9 x 10^19 cm-3 and up to 45% of them are in excitation state by impact excitation. This work has important implications for developing efficient classical and quantum light sources based on rare earth elements.

cond-mat.mtrl-sci↗

arXiv2023

Style Generation in Robot Calligraphy with Deep Generative Adversarial Networks

Robot calligraphy is an emerging exploration of artificial intelligence in the fields of art and education. Traditional calligraphy generation researches mainly focus on methods such as tool-based image processing, generative models, and style transfer. Unlike the English alphabet, the number of Chinese characters is tens of thousands, which leads to difficulties in the generation of a style consistent Chinese calligraphic font with over 6000 characters. Due to the lack of high-quality data sets, formal definitions of calligraphy knowledge, and scientific art evaluation methods, The results generated are frequently of low quality and falls short of professional-level requirements. To address the above problem, this paper proposes an automatic calligraphy generation model based on deep generative adversarial networks (deepGAN) that can generate style calligraphy fonts with professional standards. The key highlights of the proposed method include: (1) The datasets use a high-precision calligraphy synthesis method to ensure its high quality and sufficient quantity; (2) Professional calligraphers are invited to conduct a series of Turing tests to evaluate the gap between model generation results and human artistic level; (3) Experimental results indicate that the proposed model is the state-of-the-art among current calligraphy generation methods. The Turing tests and similarity evaluations validate the effectiveness of the proposed method.

cs.CV↗