arXiv subjects

Sheng Mao

Publications and source records attributed to Sheng Mao.

At least 19 recordsLinked to original sources

arXiv2026

Wrinkling of Randomly Heterogeneous Film-Substrate Systems

Wrinkling instabilities in stiff films on compliant substrates are strongly affected by spatial fluctuations in film stiffness. We develop a homogenized instability theory for one-dimensional film--substrate systems with random bending stiffness. The heterogeneous stability equation is reformulated as a Lippmann--Schwinger equation for the curvature field, and a strong-contrast expansion is derived using a local--nonlocal kernel decomposition and a cavity-field formulation. Truncation at third order yields an effective polarizability and a Dyson-type dispersion relation for predicting the critical load and wavenumber. The stiffness is modeled as an exponentially mapped Gaussian random field, allowing the required two- and three-point connected statistics to be obtained analytically. The theory is validated against generalized eigenvalue calculations and Fourier spectral simulations. Increasing stiffness contrast lowers the critical load and shifts the instability toward higher wavenumbers, producing shorter wrinkles. At weak contrast, the threshold follows the universal scaling $N_c^{(0)}-N_c\sim\varepsilon^2$, whereas at moderate and strong contrast the third-order approximation is more accurate than the second-order theory. Wavelength selection is controlled by the ratio of the dominant material wavelength $\lambda^\ast$ to the harmonic-mean reference wavelength $\lambda_H$. For $\lambda^\ast/\lambda_H<1$, the harmonic-mean model accurately predicts the wrinkle wavelength. The framework provides a mechanics-based tool for reliability assessment and design of statistically heterogeneous film--substrate systems.

cond-mat.mtrl-sci↗

arXiv2026

Grimlock: Guarding High-Agency Systems with eBPF and Attested Channels

Agentic systems increasingly run user-authored orchestration code that invokes tools, spawns subtasks, and delegates work across machines and clouds. Although this high agency is productive, it creates a security problem: identity, authorization, provenance, and delegation are often pushed into application code, where they become difficult to enforce consistently and difficult to audit. We present Grimlock, an Agent Guard that restores separation of concerns by moving trust enforcement into the sandbox substrate while leaving agent code unchanged. Grimlock uses eBPF-enforced traffic interception to ensure that sandbox communication passes through a guard, and combines it with post-handshake attestation bound to standard TLS~1.3 channel bindings. After a channel is established, the guard authorizes communication and mints short-lived, channel-bound scope tokens that capture least-privilege delegation. At the receiving side, the destination guard re-validates identity, scope, and channel binding, terminates TLS, and releases plaintext to the destination sandbox only after policy checks succeed. kTLS provides an efficient dataplane for protected communication. As a result, Grimlock offers a path toward transparent, auditable, and scope-bound agent-to-agent communication across heterogeneous multi-cloud environments, using commodity Linux primitives and without requiring changes to user-layer orchestration code.

cs.CR↗

arXiv2026

Physics-guided diffusion models for inverse design of disordered metamaterials

Disordered metamaterials are promising for programming physical properties across diverse applications, yet their inverse design remains challenging due to the non-intuitive structure-property relationships and large design spaces. Recent generative approaches, particularly diffusion models, have shown potential in high-dimensional inverse design tasks. However, existing methods typically rely on carefully crafted training objectives, such as conditional data-driven or physics-informed loss functions. Because these strategies are inherently task-specific, the model must be retrained from scratch whenever the design problem changes (e.g., different governing equations, boundary conditions, or design objectives), severely limiting their flexibility and generalization ability. In this work, we propose physics-guided diffusion models that leverage differentiable physics-based solvers to instantly guide the generative process for inverse design. Drawing inspiration from classifier guidance, we develop a sampling strategy that directly incorporates physics guidance into the reverse stochastic differential equations. Our approach enables task-adaptive generation using gradients from differentiable solvers, while the diffusion model itself needs to be trained only once on unlabeled data. Focusing on disordered foam metamaterials, we present three representative design tasks: (1) achieving target effective thermal conductivity, (2) matching desired load-displacement response, and (3) maximizing energy absorption involving fractures. In each scenario, the proposed method successfully generates foam-like geometries that fulfill the prescribed physical objectives. These results demonstrate the versatility, efficiency, and practicality of physics-guided diffusion models for tackling complex inverse design problems in disordered metamaterials and beyond.

cs.CE↗

arXiv2026

Stress-driven dynamic evolution of core-shell structured cavities with H and He in BCC-Fe under fusion conditions

Understanding the dynamic behavior of microstructures formed under fusion conditions is critical for designing high-performance structural materials for fusion reactors. Under fusion conditions, cavities of core-shell structures are formed due to the interaction between irradiation-induced vacancies and H and He atoms produced via transmutation. In this study, thermodynamic analysis and molecular dynamics simulations are combined to investigate the atomic-scale mechanisms and dynamic response of core-shell cavities formed in BCC-Fe under applied stress/strain fields. The thermodynamic analysis provides both the foundational reference for cavity structures under fusion neutron irradiation and the initial configurations for atomistic simulations. Building on this framework, atomic-scale simulations demonstrate that H and He play a decisive role in the stress-strain response and the evolution of elastic-plastic deformation within the cavities. In core-shell configurations, H atoms serve a function analogous to that in He-filled cavities, synergistically interacting with He to induce cavity deformation under mechanical loading.

cond-mat.mtrl-sci↗

arXiv2025

An exact dimension-reduced dynamic theory for developable surfaces and curve-fold origami

Curve-fold origami, composed of developable panels joined along a curved crease, exhibits rich dynamic behaviors relevant to metamaterials and soft robotic systems. Despite multiple approximated models, a comprehensive and exact dynamical theory for curve-fold origami remains absent, limiting the precise predictions of its dynamics, especially for those with wide panels. In this work, we develop an exact dimension-reduced theory that focuses on the dynamics of curve-fold origami, utilizing the intrinsic one-dimensional nature of developable surfaces. Starting from a single developable surface, we investigate the kinematics and kinetic energy of a moving developable surface. By overcoming the difficulty of describing the motion of local frames, we derive the exact velocity field of wide surfaces solely described by the motion of the reference curve, which leads to the kinetic energy of the entire surface. Owing to the one-dimensional feature, the Lagrangian of the system, composed of both kinetic and elastic energy, is a functional of the reference curve. Thus, we may variate the Lagrangian and derive a nonlinear dynamical theory for the reference curve, which comprises governing equations similar to the rod model but can precisely describe the motion of developable surfaces. The theory is validated consistently in both Lagrangian and Eulerian frameworks and is further extended to curved-fold origami modeled as a coupled bi-rod system. Utilizing our exact 1D model, we theoretically analyze the dynamical behaviors of various developable structures, revealing that the coupling of curvature and torsion along with the motion of local frames in our theory leads to the accurate modeling of arbitrarily deformed developable surfaces, which are validated by finite element analysis quantitatively.

cond-mat.soft↗

arXiv2025

Ultra-Efficient Reconstruction of Anisotropic Hyperuniform Continuous Random Fields in 2D and 3D via Generalized Spectral Filtering

Hyperuniform continuous random fields suppress large-scale fluctuations while preserving rich local disorder, making them highly attractive for next-generation photonic, thermal and mechanical materials. However, traditional reconstruction techniques often suffer from limited spectral control or excessive computational cost, especially in high-resolution 2D and 3D settings. In this work, we present an ultra-efficient generative algorithm based on generalized superellipse spectral filtering, which allows independent tuning of isotropic and anisotropic spectral envelopes without resorting to costly iterative schemes. We demonstrate our method on a comprehensive set of 2D and 3D examples, showing precise manipulation of spectral band shape and orders-of-magnitude speedup compared to existing approaches. Furthermore, we explore the effect of simple thresholding on the generated fields, analyzing the morphological features and power-spectrum characteristics of the resulting two-phase maps. Our results confirm that the proposed framework not only accelerates hyperuniform field synthesis but also provides a versatile platform for systematic study of binary microstructures derived from continuous designs. This work opens new avenues for large-scale simulation and optimized design of advanced hyperuniform materials.

cond-mat.mtrl-sci↗

arXiv2025

Exact Expansion Formalism for Transport Properties of Heterogeneous Materials Characterized by Arbitrary Continuous Random Fields

We derive an exact contrast-expansion formalism for the effective conductivity of heterogeneous materials (media) with local properties described by arbitrary continuous random fields, significantly generalizing the widely used binary-field models. The theory produces a rapidly convergent Neumann-series that, upon Gaussian closure via a Hermite expansion, yields closed-form first-, second- and third-order approximations, which achieve percent-level accuracy at first order for isotropic media. For anisotropic media, second-order approximations achieve sub-2% accuracy across a wide range of local property contrasts and correlations. Our formalism provides mathematically rigorous structure-property closures, with significant implications for the discovery and design of novel graded and architected materials with tailored transport properties.

cond-mat.mtrl-sci↗

arXiv2025

DiffLiB: High-fidelity differentiable modeling of lithium-ion batteries and efficient gradient-based parameter identification

The physics-based Doyle-Fuller-Newman (DFN) model, widely adopted for its precise electrochemical modeling, stands out among various simulation models of lithium-ion batteries (LIBs). Although the DFN model is powerful in forward predictive analysis, the inverse identification of its model parameters has remained a long-standing challenge. The numerous unknown parameters associated with the nonlinear, time-dependent, and multi-scale DFN model are extremely difficult to be determined accurately and efficiently, hindering the practical use of such battery simulation models in industrial applications. To tackle this challenge, we introduce DiffLiB, a high-fidelity finite-element-based LIB simulation framework, equipped with advanced differentiable programming techniques so that efficient gradient-based inverse parameter identification is enabled. Customized automatic differentiation rules are defined by identifying the VJP (vector-Jacobian product) structure in the chain rule and implemented using adjoint-based implicit differentiation methods. Four numerical examples, including both 2D and 3D forward predictions and inverse parameter identification, are presented to validate the accuracy and computational efficiency of DiffLiB. Benchmarking against COMSOL demonstrates excellent agreement in forward predictions, with terminal voltage discrepancies maintaining a root-mean-square error (RMSE) below 2 mV across all test conditions. In parameter identification tasks using experimentally measured voltage data, the proposed gradient-based optimization scheme achieves superior computational performance, with 96% fewer forward predictions and 72% less computational time compared with gradient-free approaches. These results demonstrate that DiffLiB is a versatile and powerful computational framework for the development of advanced LIBs.

cs.CE↗

arXiv2025

Structure-Property Relationship in Disordered Hyperuniform Materials: Microstructure Representation, Field Fluctuations and Effective Properties

Disordered hyperuniform (DHU) materials are an emerging class of exotic heterogeneous material systems characterized by a unique combination of disordered local structures and a hidden long-range order, which endow them with unusual physical properties. Here, we consider material systems possessing continuously varying local material properties $\mathcal{K}({\bf x})$ modeled via a random field. We devise quantitative microstructure representation of the material systems based on a class of analytical spectral density function ${\tilde \chi}_{_\mathcal{K}}({k})$ associated with $\mathcal{K}({\bf x})$, possessing a power-law small-$k$ scaling behavior ${\tilde \chi}_{_\mathcal{K}}({k}) \sim k^\alpha$. By controlling the exponent $\alpha$ and using a highly efficient forward generative model, we obtain realizations of a wide spectrum of distinct material microstructures spanning from hyperuniform ($\alpha>0$) to nonhyperuniform ($\alpha=0$) to antihyperuniform ($\alpha<0$) systems. We perform a comprehensive perturbation analysis to quantitatively connect the fluctuations of the local material property to the fluctuations of the resulting physical fields. In the weak-contrast limit, our first-order perturbation theory reveals that the physical fields associated with Class-I hyperuniform materials (characterized by $\alpha \ge 2$) are also hyperuniform, albeit with a lower hyperuniformity exponent ($\alpha-2$). As one moves away from this weak-contrast limit, the fluctuations of the physical field develop a diverging spectral density at the origin. We also establish an end-to-end mapping connecting the spectral density of the local material property to the overall effective conductivity of the material system via numerical homogenization.

cond-mat.mtrl-sci↗

arXiv2025

Structural and mechanical properties of W-Cu compounds characterized by a neural-network-based potential

Tungsten-copper (W-Cu) compounds are widely utilized in various industrial fields due to their exceptional mechanical properties. In this study, we have developed a neural-network-based deep potential (DP) model that covers a wide range of temperatures, ranging from 0 to 3,000 K, and pressures, varying from 0 to 10 GPa. This study presents a model trained using density functional theory data for full concentration CuxW100-x compounds. Through this model, we systematically investigate the structural and mechanical properties of W-Cu alloys and have the following findings. First, the bulk modulus (B) and Young's modulus (E) of W-Cu alloys exhibit a linear decline as the Cu content increases, indicating a softening trend in the CuxW100-x compounds as the Cu concentration rises. Second, a higher Cu content results in higher critical strain and lower critical stress for these compounds. A brittle-to-ductile transition in the deformation mode predicted is predicted at around 37.5 at. % Cu content. Third, tensile loading tests in the W-Cu gradient structure reveal that Cu-poor region serves as a barrier, hindering shear band propagation while promoting new shear band formation in the Cu-rich region. The above results from the DP model are anticipated to aid in exploring the physical mechanisms underlying the complex phenomena of W-Cu systems and contribute to the advancement of methodologies for materials simulation.

cond-mat.mtrl-sci↗

arXiv2024

Chemically reactive and aging macromolecular mixtures II: Phase separation and coarsening

In a companion paper, we put forth a thermodynamic model for complex formation via a chemical reaction involving multiple macromolecular species, which may subsequently undergo liquid-liquid phase separation and a further transition into a gel-like state. In the present work, we formulate a thermodynamically consistent kinetic framework to study the interplay between phase separation, chemical reaction and aging in spatially inhomogeneous macromolecular mixtures. A numerical algorithm is also proposed to simulate domain growth from collisions of liquid and gel domains via passive Brownian motion in both two and three spatial dimensions. Our results show that the coarsening behavior is significantly influenced by the degree of gelation and Brownian motion. The presence of a gel phase inside condensates strongly limits the diffusive transport processes, and Brownian motion coalescence controls the coarsening process in systems with high area/volume fractions of gel-like condensates, leading to formation of interconnected domains with atypical domain growth rates controlled by size-dependent translational and rotational diffusivities.

physics.bio-ph↗

arXiv2024

Learning the physics-consistent material behavior from experimentally measurable data via PDE-constrained optimization

Constitutive models play a crucial role in materials science as they describe the behavior of the materials in mathematical forms. Over the last few decades, the rapid development of manufacturing technologies have led to the discovery of many advanced materials with complex and novel behaviors, which in the meantime, have also posed great challenges for constructing accurate and reliable constitutive models of these materials. In this work, we propose a data-driven approach to construct physics-consistent constitutive models for hyperelastic materials from experimentally measurable data, with the help of PDE-constrained optimization methods. Specifically, our constitutive models are based on the physically augmented neural networks~(PANNs), which has been shown to ensure that the models are both physically consistent but also mathematically well-posed by construction. Specimens with deliberately introduced inhomogeneity are used to generate the data, i.e., the full-field displacement data and the total external load, for training the model. Using such approach, a considerably diverse pairs of stress-strain states can be explored with a limited number of simple experiments, such as uniaxial tension. A loss function is defined to measure the difference between the data and the model prediction, which is obtained by numerically solving the governing PDEs under the same geometry and loading conditions. With the help of adjoint method, we can iteratively optimize the parameters of our NN-based constitutive models through gradient descent. We test our method for a wide range of hyperelastic materials and in all cases, our methods are able to capture the constitutive model efficiently and accurately. The trained models are also tested against unseen geometry and unseen loading conditions, exhibiting strong interpolation and extrapolation capabilities.

cond-mat.mtrl-sci↗

arXiv2022

A minimal model for liquid-liquid phase separation and aging of chemically reactive macromolecular mixtures

Mixtures of several macromolecular species can lead to the formation of higher-order structures that often display non-ideal mixing behavior. In this work, we propose a minimal model of a quaternary system which considers the formation of a complex via a chemical reaction involving two macromolecular species; the complex may then phase separate from the buffer and undergo a further transition into a gel-like state over time. First, a ternary phase diagram that captures the volume fraction of each species and phases at equilibrium is constructed. Specifically, we investigate how physical parameters such as stoichiometric coefficients, molecular sizes and interaction parameters affect LLPS and aging. Finally, we analyze the thermodynamic stability of the two-phase system and identify the spinodal regions, and outline the generalization of our approach to reactive biomolecular systems with an arbitrary number of components.

cond-mat.soft↗

arXiv2022

Learning the nonlinear dynamics of soft mechanical metamaterials with graph networks

The dynamics of soft mechanical metamaterials provides opportunities for many exciting engineering applications. Previous studies often use discrete systems, composed of rigid elements and nonlinear springs, to model the nonlinear dynamic responses of the continuum metamaterials. Yet it remains a challenge to accurately construct such systems based on the geometry of the building blocks of the metamaterial. In this work, we propose a machine learning approach to address this challenge. A metamaterial graph network (MGN) is used to represent the discrete system, where the nodal features contain the positions and orientations the rigid elements, and the edge update functions describe the mechanics of the nonlinear springs. We use Gaussian process regression as the surrogate model to characterize the elastic energy of the nonlinear springs as a function of the relative positions and orientations of the connected rigid elements. The optimal model can be obtained by "learning" from the data generated via finite element calculation over the corresponding building block of the continuum metamaterial. Then, we deploy the optimal model to the network so that the dynamics of the metamaterial at the structural scale can be studied. We verify the accuracy of our machine learning approach against several representative numerical examples. In these examples, the proposed approach can significantly reduce the computational cost when compared to direct numerical simulation while reaching comparable accuracy. Moreover, defects and spatial inhomogeneities can be easily incorporated into our approach, which can be useful for the rational design of soft mechanical metamaterials.

cs.CE↗

arXiv2021

Liquid demixing in elastic networks: cavitation, permeation, or size selection?

Demixing of multicomponent biomolecular systems via liquid-liquid phase separation (LLPS) has emerged as a potentially unifying mechanism governing the formation of several membrane-less intracellular organelles ("condensates"), both in the cytoplasm (e.g., stress granules) and in the nucleoplasm (e.g., nucleoli). While both in vivo experiments and studies of synthetic systems demonstrate that LLPS is strongly affected by the presence of a macromolecular elastic network, a fundamental understanding of the role of such networks on LLPS is still lacking. Here we show that, upon accounting for capillary forces responsible for network expulsion, small-scale heterogeneity of the network, and its nonlinear mechanical properties, an intriguing picture of LLPS emerges. Specifically, we predict that, in addition to the experimentally observed cavitated droplets which fully exclude the network, two other phases are thermodynamically possible: elastically arrested, size-limited droplets at the network pore scale, and network-including macroscopic droplets. In particular, pore size-limited droplets may emerge in chromatin networks, with implications for structure and function of nucleoplasmic condensates.

cond-mat.soft↗

arXiv2020

Designing morphology of separated phases in multicomponent liquid mixtures

Phase separation of multicomponent liquid mixtures plays an integral part in many processes ranging from industry to cellular biology. In many cases the morphology of coexisting phases is crucially linked to the function of the separated mixture, yet it is unclear what determines morphology when multiple phases are present. We developed a graph theory approach to predict the topology of coexisting phases from a given set of surface energies (forward problem), enumerate all topologically distinct morphologies, and reverse engineer conditions for surface energies that produce the target morphology (inverse problem).

cond-mat.soft↗

arXiv2019

Non-uniform growth and surface friction determine bacterial biofilm morphology on soft substrates

During development, organisms acquire three-dimensional shapes with important physiological consequences. While the basic mechanisms underlying morphogenesis are known in eukaryotes, it is often difficult to manipulate them in vivo. To circumvent this issue, here we present a study of developing Vibrio cholerae biofilms grown on agar substrates in which the spatiotemporal morphological patterns were altered by varying the agar concentration. Expanding biofilms are initially flat, but later experience a mechanical instability and become wrinkled. Whereas the peripheral region develops ordered radial stripes, the central region acquires a zigzag herringbone-like wrinkle pattern. Depending on the agar concentration, the wrinkles initially appear either in the peripheral region and propagate inward (low agar concentration) or in the central region and propagate outward (high agar concentration). To understand these experimental observations, we developed a model that considers diffusion of nutrients and their uptake by bacteria, bacterial growth/biofilm matrix production, mechanical deformation of both the biofilm and the agar, and the friction between them. Our model demonstrates that depletion of nutrients beneath the central region of the biofilm results in radially-dependent growth profiles, which in turn, produce anisotropic stresses that dictate the morphology of wrinkles. Furthermore, we predict that increasing surface friction (agar concentration) reduces stress anisotropy and shifts the location of the maximum compressive stress, where the wrinkling instability first occurs, toward the center of the biofilm, in agreement with our experimental observations. Our results are broadly applicable to bacterial biofilms with similar morphologies and also provide insight into how other bacterial biofilms form distinct wrinkle patterns.

physics.bio-ph↗

arXiv2018

Phase behavior and morphology of multicomponent liquid mixtures

Multicomponent systems are ubiquitous in nature and industry. While the physics of few-component liquid mixtures (i.e., binary and ternary ones) is well-understood and routinely taught in undergraduate courses, the thermodynamic and kinetic properties of $N$-component mixtures with $N>3$ have remained relatively unexplored. An example of such a mixture is provided by the intracellular fluid, in which protein-rich droplets phase separate into distinct membraneless organelles. In this work, we investigate equilibrium phase behavior and morphology of $N$-component liquid mixtures within the Flory-Huggins theory of regular solutions. In order to determine the number of coexisting phases and their compositions, we developed a new algorithm for constructing complete phase diagrams, based on numerical convexification of the discretized free energy landscape. Together with a Cahn-Hilliard approach for kinetics, we employ this method to study mixtures with $N=4$ and $5$ components. We report on both the coarsening behavior of such systems, as well as the resulting morphologies in three spatial dimensions. We discuss how the number of coexisting phases and their compositions can be extracted with Principal Component Analysis (PCA) and K-Means clustering algorithms. Finally, we discuss how one can reverse engineer the interaction parameters and volume fractions of components in order to achieve a range of desired packing structures, such as nested `Russian dolls' and encapsulated Janus droplets.

cond-mat.soft↗