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Huajian Gao

Publications and source records attributed to Huajian Gao.

At least 19 recordsLinked to original sources

Membrane Tension Governs Particle Wrapping-Unwrapping Transitions and Stalling

Membrane wrapping controls nanoparticle uptake during endocytosis, whereas the reverse process of membrane unwrapping accompanies particle expulsion and membrane fusion events. Existing theoretical descriptions typically focus on adhesion and bending energies within the particle membrane contact region and often neglect the deformation energy of the membrane outside the contact zone. This approximation is valid only in the limit of vanishing membrane tension, where the non contact membrane assumes a catenoid like configuration with negligible bending energy. However, at finite tension the deformation of the non contact membrane becomes a dominant energetic contribution. Here we show that this tension dependent non contact energy governs the progression of particle wrapping. By analysing the variation of the total membrane energy with wrapping degree, we uncover a competition between particle adhesion, membrane tension and particle size that determines whether wrapping proceeds, stalls, or reverses into spontaneous unwrapping. This framework reveals a stalling boundary separating regimes of particle uptake and expulsion. To capture the non contact deformation efficiently, we derive a compact phenomenological approximation that accurately reproduces the full numerical solution of the membrane shape. The resulting energetic map provides a unified physical description of particle wrapping and unwrapping, with implications for endocytosis, membrane fusion, and nanoparticle design.

physics.bio-ph↗

In-context modeling as a retrain-free paradigm for foundation models in computational science

Building models that generalize across physical systems without retraining remains a central challenge in computational science. Here we introduce In-Context Modeling (ICM), a retrain-free paradigm that infers physical relationships directly from observational fields. Rather than encoding system-specific behavior in fixed parameters, ICM assimilates measurements as physical context and performs inference through a single forward pass. Trained in a physics-informed, label-free manner using governing equations, a single model generalizes across unseen materials, geometries, and loading conditions. Demonstrated on hyperelasticity, ICM integrates with finite-element simulations and is validated using experimental full-field measurements. Moreover, performance improves with increasing data diversity and computational budget, exhibiting favorable scaling behavior analogous to foundation models. By recasting physical modeling as in-context inference, this work establishes a transferable paradigm for retrain-free scientific learning and a foundation for scalable modeling across computational science.

cs.CE↗

AlloyVAE: A generative model for complex probabilistic field-to-field relationships in alloys

The inherent compositional heterogeneity of multi-principal element alloys (MPEAs) gives rise to complex, spatially varying mechanical fields that cannot be uniquely determined from coarse-grained composition descriptors. This non-uniqueness introduces intrinsically probabilistic structure-property relationships, posing a fundamental challenge to conventional deterministic modeling and machine learning approaches that collapse such mappings into average predictions. Here, we present AlloyVAE, a physics-informed generative framework that learns the full conditional distribution of mechanical fields from microstructural inputs. Built upon a conditional variational autoencoder architecture, the model incorporates learned smoothing operators to enhance functional regularity and a self-consistency mechanism to enforce physical plausibility. Trained on atomistic simulation data, AlloyVAE accurately predicts distributions of residual stress fields from composition and short-range order, and enables the generation of multiple physically consistent realizations under identical input conditions. Beyond forward prediction, the framework supports inverse design by optimizing composition fields to achieve targeted mechanical responses, and is extensible to coupled mappings involving eigenstrain. By capturing one-to-many structure-property relationships in heterogeneous materials, this work establishes a probabilistic paradigm for materials modeling and design, providing a scalable alternative to conventional simulations for navigating high-dimensional compositional spaces.

cond-mat.mtrl-sci↗

Gaussian Ensemble Topology (GET): A New Explicit and Inherently Smooth Framework for Manufacture-Ready Topology Optimization

We introduce the Gaussian Ensemble Topology (GET) method, a new explicit and manufacture-ready framework for topology optimization in which design geometries are represented as superpositions of anisotropic Gaussian functions. By combining explicit Gaussian descriptions with a level-set-like Heaviside projection, GET inherently generates smooth, curvature-continuous designs without requiring post-processing steps such as mesh or corner smoothing and feature extraction. The method is validated on standard compliance-minimization and compliant mechanism benchmarks in two and three dimensions. The optimized designs achieve objective values comparable to those obtained with classical Moving Morphable Component (MMC) approaches, but with geometrically consistent, refined boundaries. Numerical examples demonstrate additional advantages of the GET framework, including mesh independence inherent to explicit parameterizations, strong geometric expressiveness, and effective control over smoothness, discreteness, and structural complexity through parameter tuning. As a robust and manufacture-ready approach to explicit topology optimization, GET opens avenues for tackling advanced and complex design problems.

cs.CE↗

Discrete Differential Geometry for Simulating Nonlinear Behaviors of Flexible Systems: A Survey

Flexible slender structures such as rods, ribbons, plates, and shells exhibit extreme nonlinear responses bending, twisting, buckling, wrinkling, and self contact, that defy conventional simulation frameworks. Discrete Differential Geometry (DDG) has emerged as a geometry first, structure preserving paradigm for modeling such behaviors. Unlike finite element or mass spring methods, DDG discretizes geometry rather than governing equations, allowing curvature, twist, and strain to be defined directly on meshes. This approach yields robust large deformation dynamics, accurate handling of contact, and differentiability essential for inverse design and learning based control. This review consolidates the rapidly expanding landscape of DDG models across 1D and 2D systems, including discrete elastic rods, ribbons, plates, and shells, as well as multiphysics extensions to contact, magnetic actuation, and fluid structure interaction. We synthesize applications spanning mechanics of nonlinear instabilities, biological morphogenesis, functional structures and devices, and robotics from manipulation to soft machines. Compared with established approaches, DDG offers a unique balance of geometric fidelity, computational efficiency, and algorithmic differentiability, bridging continuum rigor with real time, contact rich performance. We conclude by outlining opportunities for multiphysics coupling, hybrid physics data pipelines, and scalable GPU accelerated solvers, and by emphasizing DDG role in enabling digital twins, sim to real transfer, and intelligent design of next generation flexible systems.

cond-mat.soft↗

A scaling law for large-deformation contact in soft materials

Compression of soft bodies is central to biology, materials science, and robotics, yet existing contact theories break down at large deformations. Here, we develop a general framework for soft-body compression by extending the method of dimensionality reduction into the nonlinear regime. Analytical solutions for contact force and radius are derived and validated against finite element simulations and experiments on canonical geometries (cone, hemisphere, cylinder), achieving high accuracy up to 50% compression. From this framework emerges a universal scaling law that unifies the nonlinear force-displacement response across diverse shapes and even irregular soft objects such as gummy candies. Leveraging this principle, we design a vision-based tactile sensor that reconstructs real-time pressure maps and enables delicate robotic manipulation of fragile items. By bridging nonlinear contact mechanics with practical sensing, this work both advances fundamental understanding of large-strain mechanics and opens a route to robust tactile technologies for soft robotics and biomedical applications.

cond-mat.soft↗

Gear-based Metamaterials for Extraordinary Bandgap Tunability

Metamaterials can be engineered with tunable bandgaps to adapt to dynamic and complex environments, particularly for controlling elastic waves and vibration. However, achieving wide-range, seamless, reversible, in-situ and robust tunability remains challenging and often impractical due to limitations in bandgap mechanisms and design principles. Here, we introduce gear-based metamaterials with unprecedented bandgap tunability. Our approach leverages Taiji planetary gear systems as variable-frequency local resonators, which allows the metamaterial to seamlessly modulate its bandgap's center frequency by 3-7 times (e.g. shifting from 250-430 Hz to 1400-2000 Hz), surpassing existing methods. Notably, this is achieved without pre-deformation or major changes to its static stiffness in the wave propagation direction, ensuring robust in-situ tunability and smooth control even under heavy static loads. This enables adaptable wave manipulation for versatile smart platforms.

physics.app-ph↗

Universal Scaling Laws for Deep Indentation Beyond the Hertzian Regime

Deep indentation of soft materials is ubiquitous across scales in nature and engineering, yet accurate predictions of contact behaviors under extreme deformations ($δ/R > 1$) remain elusive due to geometric and material nonlinearities. Here, we investigate the indentation of rigid spheres into soft elastic substrates, resolving the highly nonlinear regime where the sphere becomes fully submerged. A universal geometric mapping approach reveals Hertz-type pressure distributions in the deformed configuration, validated by FEA. Closed-form solutions for contact force and radius agree with simulations up to $δ/R = 2.5$. Experiments spanning soft polymers (Ecoflex, PDMS), food substrates (tofu), and biological tissues (octopus) validate the derived scaling law for hyperelastic materials. Our results establish a universal framework for extreme mechanical interactions, with applications in soft robotics, bioengineered systems, and tissue mechanics.

cond-mat.soft↗

A tutorial on simulating nonlinear behaviors of flexible structures with the discrete differential geometry (DDG) method

Flexible elastic structures, such as beams, rods, ribbons, plates, and shells, exhibit complex nonlinear dynamical behaviors that are central to a wide range of engineering and scientific applications, including soft robotics, deployable structures, and biomedical devices. While various numerical methods have been developed to simulate these behaviors, many conventional approaches struggle to simultaneously capture geometric and material nonlinearities, as well as nonlinear external interactions, particularly in highly deformable and dynamically evolving systems. The Discrete Differential Geometry (DDG) method has emerged as a robust and efficient numerical framework that intrinsically preserves geometric properties, accommodates material nonlinearity, and accurately models interactions with external environments and fields. By directly discretizing geometric and mechanical quantities, DDG provides an accurate, stable, and efficient approach to modeling flexible structures, addressing key limitations of traditional numerical methods. This tutorial provides a systematic introduction to the DDG method for simulating nonlinear behaviors in flexible structures. It covers DDG theory, simulation frameworks, and MATLAB implementation, with examples spanning dynamic systems, geometric and material nonlinearities, and external interactions like magnetics and fluids, culminating in practical insights and future directions. By offering a comprehensive and practical guide, together with open-source MATLAB code, this tutorial aims to facilitate the broader adoption of DDG-based numerical tools among researchers and engineers in computational mechanics, applied mathematics, and structural design.

cond-mat.soft↗

Rubber-to-glass adhesion between a rigid sphere and a shape memory polymer substrate of finite thickness

Shape memory polymers (SMPs) are emerging as innovative smart adhesive materials with broad application potential. Compared to conventional elastomeric adhesives, SMP adhesives are distinguished by the so-called rubber-to-glass (R2G) adhesion, which involves contact in the rubbery state followed by detachment in the glassy state. This process, through a shape-locking effect, enhances adhesion strength by more than an order of magnitude compared to conventional adhesive contact. Here, we investigate the fundamental problem of a rigid sphere undergoing R2G adhesion with an SMP substrate of finite thickness through experiments, finite element (FE) simulations, and theoretical modeling. It is demonstrated that during press-in, the contact problem can be modeled as a rigid oblate spheroid contacting an infinite substrate, while the pull-off process can be described by a modified ball-and-socket model. These equivalent models yield practically useful analytical solutions for the contact radius during press-in and the R2G adhesion force during pull-off. A critical thickness-to-contact-radius ratio of around 5 is identified, below which the thickness effect becomes significant. These insights provide valuable guidance for the design and application of SMP-based smart adhesives.

cond-mat.soft↗

Micropores can enhance intrinsic fracture energy of hydrogels

It is widely known that hydrogels, a class of soft materials made of a polymer chain network, are prone to fatigue failure. To understand the underlying mechanism, here we simulate polymer scission and fatigue initiation in the vicinity of a crack tip in a two-dimensional chain network. For a network without pores, our findings reveal that polymer scission can take place across multiple layers of chains, rather than just a single layer as assumed in the classical Lake-Thomas theory, in consistency with previus studies. For a network with a high density of micropores, our results demonstrate that the pores can substantially enhance the intrinsic fracture energy of the network in direct proportion to the pore size. The underlying mechanism is attributed to pore-pore interactions which lead to a relatively uniform distribution of cohesive energy ahead of the crack tip. Our model suggests that micropores could be a promising strategy for improving the intrinsic fracture energy of hydrogels and that natural porous tissues may have evolved for enhanced fatigue resistance.

cond-mat.soft↗

Amorphous alloys surpass E/10 strength limit at extreme strain rates

Theoretical predictions of the ideal strength of materials range from E/30 to E/10 (E is Young's modulus). However, despite intense interest over the last decade, the value of the ideal strength that can be attained experimentally for metals remains a mystery (1-5). In this study, we demonstrated the unprecedented strength of an amorphous Cu-Zr alloy that surpassed the E/10 limit. Laser-induced shock experiments were conducted on Cu50Zr50 to explore its strength and failure mechanisms at ultrahigh strain rates. The material demonstrated a high spall strength of 9.8 GPa, approximately 1/13 of its P-wave modulus (~ E/6), at strain rates greater than 10^7 s^-1, which sets a new record for the elastic limit of metallic materials. Electron microscopy and large-scale molecular dynamics simulations revealed that void nucleation and growth, not shear-banding, comprised the major failure mechanism for metallic glasses at extremely fast strain rates. A new model for void formation under the control of surface energy explained the rate dependence of the material strength. The results of this study reveal new possible ways to use the amorphous phase in nanostructured metals in future applications under demanding mechanical conditions.

cond-mat.mtrl-sci↗

On stress concentration in nanotwinned metals

In their papers, Lu et al. analysed experimental data from in-situ transmission electron microscopy (TEM) and concluded that local stress concentration at grain boundary-twin boundary (GB-TB) intersections in nanotwinned (nt-) metals increases as the TB thicknesses is reduced (below $λ$ $\leq$ 18 nm). However, here, by analysing the raw experimental data in Lu et al. together with finite element simulations and dimensional analysis, we show that the measurement of local stress concentration in a nt-material is highly sensitive to the adopted averaging box size due to the high stress gradient near GB-TB intersections as well as to the TB location in the nt-grain. By careful analysis of the raw data, a completely reverse trend, which is consistent with well-established laws of continuum mechanics, can be captured.

cond-mat.mtrl-sci↗

A general criterion for solid instability and its application to creases

A general force-perturbation-based criterion for solid instability is proposed, which can predict instability including crease without priori knowledge of instability configuration. The crease instability is analyzed in detail, we found that the occurrence of solid instability does not always correspond to the non-positive definiteness of global stiffness matrix. An element stiffness-based criterion based on material stiffness is proposed as a stronger criterion in order to fast determine the occurrence of instability. This criterion has been shown to degenerate into the criterion for judging instability of certain known phenomena, such as necking and shear band phenomena. Besides, instability in strongly anisotropic materials is also predicted by the element stiffness-based criterion.

cond-mat.soft↗

Topological design of graphene

Topological defects (e.g. pentagons, heptagons and pentagon-heptagon pairs) have been widely observed in large scale graphene and have been recognized to play important roles in tailoring the mechanical and physical properties of two-dimensional materials in general. Thanks to intensive studies over the past few years, optimizing properties of graphene through topological design has become a new and promising direction of research. In this chapter, we review some of the recent advances in experimental, computational and theoretical studies on the effects of topological defects on mechanical and physical properties of graphene and applications of topologically designed graphene. The discussions cover out-of-plane effects, inverse problems of designing distributions of topological defects that make a graphene sheet conform to a targeted three-dimensional surface, grain boundary engineering for graphene strength, curved graphene for toughness enhancement and applications in engineering energy materials, multifunctional materials and interactions with biological systems. Despite the rapid developments in experiments and simulations, our understanding on the relations between topological defects and mechanical and physical properties of graphene and other 2D materials is still in its infancy. The intention here is to draw the attention of the research community to some of the open questions in this field.

cond-mat.mes-hall↗

A Review on Mechanics and Mechanical Properties of 2D Materials - Graphene and Beyond

Since the first successful synthesis of graphene just over a decade ago, a variety of two-dimensional (2D) materials (e.g., transition metal-dichalcogenides, hexagonal boron-nitride, etc.) have been discovered. Among the many unique and attractive properties of 2D materials, mechanical properties play important roles in manufacturing, integration and performance for their potential applications. Mechanics is indispensable in the study of mechanical properties, both experimentally and theoretically. The coupling between the mechanical and other physical properties (thermal, electronic, optical) is also of great interest in exploring novel applications, where mechanics has to be combined with condensed matter physics to establish a scalable theoretical framework. Moreover, mechanical interactions between 2D materials and various substrate materials are essential for integrated device applications of 2D materials, for which the mechanics of interfaces (adhesion and friction) has to be developed for the 2D materials. Here we review recent theoretical and experimental works related to mechanics and mechanical properties of 2D materials. While graphene is the most studied 2D material to date, we expect continual growth of interest in the mechanics of other 2D materials beyond graphene.

cond-mat.mes-hall↗

New Insights on Stacking Fault Behavior in Twin Induced Plasticity from Meta-Atom Molecular Dynamics Simulations

There is growing interest in promoting deformation twinning for plasticity in advanced materials, as highly organized twin boundaries are beneficial to better strength-ductility combination in contrast to disordered grain boundaries. Twinning deformation typically involves the kinetics of stacking faults, its interaction with dislocations, and dislocation - twin boundary interactions. While the latter has been intensively investigated, the dynamics of stacking faults has been less known. In this work, we report several new insights on the stacking fault behavior in twin induced plasticity from our meta-atom molecular dynamics simulation: The stacking fault interactions are dominated by dislocation reactions taking place spontaneously, different from the proposed mechanism in literatures; The competition among generating a single stacking fault, a twinning partial and a trailing partial dislocation is dependent on a unique parameter, i.e. stacking fault energy, which in turn determines deformation twinning behaviors. The complex twin-slip and twin-dislocation interactions demonstrate the dual role of deformation twins as both dislocation barrier and storage, potentially contributing to the high strength and ductility of advanced materials like TWIP steels where deformation twinning dominated plasticity accounts for the superb strength-ductility combination.

cond-mat.mtrl-sci↗

Gas-like adhesion of two-dimensional materials onto solid surfaces

The adhesion of two-dimensional (2D) materials to other surfaces is so far believed to be a solid-solid mechanical contact. Here, we conduct both atomistic simulations and theoretical modeling to show that there exists a reversible conversion of energy between thermal and mechanical work in the attachment/detachment of 2D materials on/off a surface, indicating that 2D materials adhesion is fundamentally like gas adsorption rather than solid adhesion. We reveal that the underlying mechanism of this intriguing gas-like adhesion for 2D materials is the entropy difference between their freestanding and adhered states. Both the theoretical model and atomistic simulations predict that adhesion induced entropy difference increases with increasing adhesion energy and decreasing equilibrium binding distance. The present findings provide a fundamental guidance toward understanding the adhesion of 2D materials, which is important for designing 2D materials based devices and may have general implications for nanoscale efficient energy conversion.

cond-mat.mtrl-sci↗