arXiv subjects

Safa Jamali

Publications and source records attributed to Safa Jamali.

At least 19 recordsLinked to original sources

arXiv2026

Rheology and Dynamic Arrest in Colloidal Depletion Gels Mediated by Surface Brush Density

We use the density of surface-grafted polymers as a geometry-preserving control parameter for tuning the rheology of colloidal depletion gels. Reducing brush density accelerates gelation and produces gels with higher plateau storage modulus and yield stress. This mechanical enhancement is not accompanied by increased local densification; low-brush networks exhibit lower average contact number and reduced spatial heterogeneity while displaying stronger elastic responses than their high-brush counterparts. Our findings demonstrate a reduced coordination threshold to form elastic nodes in the low-brush gel network. In addition, low-brush gels relax more slowly, accumulate less creep deformation, and exhibit lower effective noise temperatures within the Soft Glassy Rheology framework. These results establish surface-brush density as an experimentally accessible control parameter for colloidal depletion gel rheology with coupled changes in effective attraction, network architecture, and contact kinematics.

cond-mat.soft↗

arXiv2026

Size matters more than packing in bimodal colloidal gel compositions

Colloidal gels are frequently modeled as monodisperse particle networks, although practical formulations commonly contain particles with multiple characteristic sizes. Here, we use large-scale, hydrodynamically resolved simulations of colloidal depletion gels to isolate the effects of particle size and local packing in bimodal systems with a small-to-large size ratio of 1:2. Increasing the large-particle fraction introduces new heterotypic angular motifs and substantially increases the fraction of bonds participating in tetrahedral structures, with a maximum at intermediate composition. However, these additional rigid motifs do not reorganize into larger or more highly connected tetrahedral aggregates. The mean coordination and characteristic aggregate size remain nearly composition independent. By contrast, the void and cluster-size distributions coarsen systematically as the large-particle fraction increases. These mesoscale distributions largely collapse when normalized by a composition-dependent particle length scale, indicating that changes in composition primarily rescale gel architecture rather than producing distinct rigid-network topologies. An elastic modulus estimated using Cauchy-Born theory similarly follows this effective length scale more closely than the abundance of local tetrahedral motifs. These results show that, for moderate size disparity, particle size controls the structural scale and predicted mechanical response of bimodal colloidal gels more strongly than enhanced local packing.

cond-mat.soft↗

arXiv2026

Bimodal colloids highlight the structural mirror of rigidity percolation and yielding

In metastable particulate gels, it is tempting to believe that the dynamic similarities between the fluid-to-solid non-linear phase transition of rigidity percolation and the solid-to-fluid transition that occurs during yielding represent mirror images of the same continuous process. Even though these behaviors are clearly dynamically similar, their multi-scale nature makes it difficult to determine if they could also follow a unified structural pathway. We know from model monodisperse colloidal gels that both yielding and the elastic modulus seem to be heavily influenced by a small subset of topologically distinct singly-connected bridges linking mesoscale features. Here we use particle simulations to examine the participation of different classes of particle-level bonds and their contributions to the bulk mechanical response. We find that rigidity is disproportionately supported by singly connected intercluster bridges, whereas yielding localizes at bonds with high edge-betweenness centrality (EBC); strikingly, these independently identified populations substantially overlap and perform comparable mechanical roles. Bimodality exposes this correspondence by concentrating large-particle contacts in both populations, thereby providing a compositional label for the common backbone. Thus, rigidity and yielding are opposing mechanical manifestations of the same mesoscale structure: the intercluster bottlenecks that establish rigidity are also the sites at which rigidity is preferentially lost.

cond-mat.soft↗

arXiv2026

Isosbestic points in time resolved SAXS: from spectroscopic analogy to model free structural markers during colloidal gelation

Gelation is the transition from a fluid state into a system-spanning, out of equilibrim soft-solid network through a hierarchical process that couples local particle interactions to mesoscopic clustering and global connectivity. In time-resolved small-angle X-ray scattering (SAXS), isosbestic points -- scattering wavevectors where scattering intensity remains invariant -- emerge during this transformation, yet their physical meaning has remained unclear. Here, we show that two isosbestic points, $q_1$ and $q_2$, observed during salt-induced gelation of Ludox colloids, reflect fundamental structural constraints rather than a two-species interconversion. The high-$q$ point $q_2$ is a universal geometric marker, determined by particle contact distances, while the low-$q$ point $q_1$ arises from Porod invariant conservation and separates rapidly arrested local clusters from the growing mesoscopic network. By decomposing the Porod invariant across the reciprocal-space regions defined by these points, we define a dimensionless parameter, $\Phi(t/t_g)$, whose sigmoidal evolution provides a simple, model-free, scale-resolved fingerprint of gelation. Together with the combined evolution of $S(q_{\min},t)$ and $S(q \rightarrow 0,t)$, these results establish a quantitative model free framework linking local structuring, global connectivity, and scattering signatures, clarifying the role of isosbestic points in soft-matter transformations.

cond-mat.soft↗

arXiv2026

Shear-Induced Structural Convergence but Formation-History-Dependent Yielding in Sequentially Gelled Binary Colloidal Networks

Multicomponent colloidal gels can exhibit mechanical responses that depend not only on interaction strengths but also on the temporal pathway by which their networks form. Here, we use particle-based simulations to investigate the steady-shear deformation of binary colloidal gels assembled by sequential gelation with tunable delay time and dominant interspecies attractions. Although varying the gelation delay produces markedly different quiescent morphologies, ranging from well-mixed networks to coarse shell-core structures, steady shear drives the systems toward structurally convergent, mixed states as quantified by cluster, connected-component, and coordination analyses. This structural convergence, however, does not imply rheological equivalence. The transient stress response remains strongly dependent on gelation delay and interspecies attraction strength. For moderate interspecies attractions, increasing delay enhances the stress overshoot, particularly at high shear rates. For stronger interspecies attractions, initially heterogeneous gels exhibit two-step yielding at low shear rates, indicating distinct deformation and restructuring processes. These results show that sequential gelation can imprint a persistent rheological memory in binary colloidal gels, even when shear substantially erases differences in common structural descriptors.

cond-mat.soft↗

arXiv2026

Hierarchical Multi-Fidelity Learning for Predicting Three-Dimensional Flame Wrinkling and Turbulent Burning Velocity

High-fidelity experimental characterization of turbulent premixed flames remains limited by the cost and complexity of advanced diagnostics, particularly under elevated pressures and intense turbulence where measurements of coupled flame morphology and burning dynamics are sparse. Here, we develop a hierarchical multi-fidelity neural network framework (MuFiNNs) to address this challenge by integrating sparse high-fidelity experimental data with structured low-fidelity representations encoding dominant physical trends. The framework combines hierarchical low-fidelity construction with nonlinear multi-fidelity correction to learn coupled geometric and reactive flame behavior while recovering discrepancies that simplified models alone cannot capture. The methodology is applied to expanding turbulent premixed flames to predict three-dimensional flame wrinkling dynamics and turbulent mass burning velocity across varying fuels, pressures, and turbulence intensities. Using experimentally informed low-fidelity trend models with sparse high-fidelity measurements, MuFiNNs accurately reconstruct observed flame behavior, enable interpolation across unseen operating conditions, and demonstrate robust extrapolation beyond the training domain. Importantly, the framework remains effective in noisy, weakly structured, or experimentally inaccessible regimes where conventional data-driven approaches often fail. These results show that hierarchical multi-fidelity learning provides a scalable and physically grounded strategy for predictive combustion modeling in data-limited regimes. More broadly, this work establishes multi-fidelity scientific machine learning as a practical framework for extracting physically meaningful predictive models from sparse experiments, particularly for instability-dominated and turbulence-sensitive reactive flows where high-fidelity data acquisition is demanding.

cs.LG↗

arXiv2026

From Double Colloidal Networks to Core-Shell and Mixed Composites through Sequential Gelation

Multicomponent gel systems have garnered much interest due to their compelling mechanical properties in the past decade. Yet, some mechanisms associated with multicomponent gels, such as sequential gelation, have been explored primarily in the context of chemical nonreversible polymeric and protein gels than in physical reversible colloidal ones. In this study, we use mesoscale simulation techniques to model the sequential gelation of two-component colloidal systems whose components' interspecies and intraspecies electrostatic interactions can be modified independently. We show that by simply leveraging temporal control and interspecies interactions, we can construct markedly different networks; from double networks to mixed and core-shell composite structures of varying coarseness and heterogeneity natures. These findings present a compelling case for further exploration of multicomponent colloidal systems.

cond-mat.soft↗

arXiv2026

Brush-mediated angular constraints reshape structure, rigidity, and percolation in colloidal depletion gels

Colloidal gels, like many other soft and disordered solids derive their mechanical properties not only from the strength of interparticle attraction, but also from the symmetry of the forces that constrain particle motion. While non-central interactions are known to profoundly alter rigidity and elasticity, they are typically introduced through particle anisotropy, surface roughness, or patchy interactions, obscuring their independent role. Here we demonstrate a minimal and geometry-preserving route to emergent non-central forces in colloidal gels by reducing the density of surface-grafted polymer brushes. At low brush density, partial brush interpenetration introduces an effective angular bending rigidity at particle contacts, despite fully isotropic particle geometry. This emergent constraint suppresses local densification, stabilizes low-coordination networks, and produces highly ramified gel structures with enhanced elasticity. Combining experiments, simulations, and mean-field theory, we show that these non-central constraints reorganize structure and mechanics across length scales, shifting gelation boundaries and increasing the elastic modulus by nearly a factor of three. Our results establish surface brush density as a generic control parameter for programming interaction symmetry in soft particulate matter, with implications for rigidity, percolation, and mechanical design in disordered systems.

cond-mat.soft↗

arXiv2026

Non-local physics-informed neural networks for forward and inverse solutions of granular flows

Dense granular flows exhibit nonlocal effects due to stress transmission in microplastic events, especially in quasi-static or slowly sheared regions. Hence, traditional local rheological models fail to capture spatial cooperativity effects that are prominent in many granular systems. The nonlocal granular fluidity (NGF) model addresses this limitation by introducing a diffusive-like partial differential equation for a fluidity field, governed by a key material-dependent parameter: the nonlocal amplitude A. However, determining A from experiments or simulations is known to be difficult and typically requires extensive calibration across multiple geometries. In this work, we present a data-driven platform based on Physics-Informed Neural Networks (PINNs) embedded with the NGF model, capable of solving granular flows in a forward or inverse manner. We show that once trained on transient flow fields, these non-local PINNs can readily infer the material parameters, as well as the pressure and stress fields. These data-driven frameworks allow for accurate recovery of small variations in the nonlocal amplitude, A, which lead to sharp bifurcation-like transitions in the flow field. This approach demonstrates the feasibility of data-driven parameter inference in complex nonlocal models and opens up new possibilities for characterizing granular materials from sparse experimental observations.

cond-mat.soft↗

arXiv2026

A Minimal Nonlocal Theory of Thixotropic Flow

Dense amorphous materials exhibit both nonlocal flow cooperativity and pronounced history dependence, yet existing continuum models capture only one of these features at a time. Nonlocal rheologies are intrinsically memoryless, while thixotropic models remain local. Here we introduce a coupling between structural memory and nonlocal fluidity to include aging and rejuvenation in nonlocal granular fluidity. The resulting model reproduces hysteresis in shear-rate sweeps and delayed yielding in creep, while preserving nonlocal flow profiles. By introducing memory augmented non local granular fluidity, MNGF, we show that nonlocality alone cannot encode history, and memory alone cannot encode spatial cooperativity, but their coupling is essential and minimal. These results demonstrate that memory and nonlocality must be treated jointly to describe history dependent flows, and provide a unified framework for modeling time-dependent rheology in dense amorphous materials.

cond-mat.soft↗

arXiv2025

RheOFormer: A generative transformer model for simulation of complex fluids and flows

The ability to model mechanics of soft materials under flowing conditions is key in designing and engineering processes and materials with targeted properties. This generally requires solution of internal stress tensor, related to the deformation tensor through nonlinear and history-dependent constitutive models. Traditional numerical methods for non-Newtonian fluid dynamics often suffer from prohibitive computational demands and poor scalability to new problem instances. Developments in data-driven methods have mitigated some limitations but still require retraining across varied physical conditions. In this work, we introduce Rheological Operator Transformer (RheOFormer), a generative operator learning method leveraging self-attention to efficiently learn different spatial interactions and features of complex fluid flows. We benchmark RheOFormer across a range of different viscometric and non-viscometric flows with different types of viscoelastic and elastoviscoplastic mechanics in complex domains against ground truth solutions. Our results demonstrate that RheOFormer can accurately learn both scalar and tensorial nonlinear mechanics of different complex fluids and predict the spatio-temporal evolution of their flows, even when trained on limited datasets. Its strong generalization capabilities and computational efficiency establish RheOFormer as a robust neural surrogate for accelerating predictive complex fluid simulations, advancing data-driven experimentation, and enabling real-time process optimization across a wide range of applications.

cs.LG↗

arXiv2025

A detailed and comprehensive account of fractional Physics-Informed Neural Networks: From implementation to efficiency

Fractional differential equations are powerful mathematical descriptors for intricate physical phenomena in a compact form. However, compared to integer ordinary or partial differential equations, solving fractional differential equations can be challenging considering the intricate details involved in their numerical solutions. Robust data-driven solutions hence can be of great interest for solving fractional differential equations. In the recent years, fractional physics-informed neural network has appeared as a platform for solving fractional differential equations and till now, efforts have been made to improve its performance. In this work, we present a fully detailed interrogation of fractional physics-informed neural networks with different foundations to solve different categories of fractional differential equations: fractional ordinary differntial equation, as well as two and three dimensional fractional partial differential equations. These equations are solved employing two numerical methods based on the Caputo formalism. We show that these platforms are generally able to accurately solve the equations with minor discrepancies at initial times. Nonetheless, since in Caputo formalism, the value of a fractional derivative at each point requires the function's value in all of its previous history, it is computationally burdensome. Here, we discuss strategies to improve accuracy of fractional physics-informed neural networks solutions without imposing heavy computational costs.

math.AP↗

arXiv2024

UniFIDES: Universal Fractional Integro-Differential Equation Solvers

The development of data-driven approaches for solving differential equations has been followed by a plethora of applications in science and engineering across a multitude of disciplines and remains a central focus of active scientific inquiry. However, a large body of natural phenomena incorporates memory effects that are best described via fractional integro-differential equations (FIDEs), in which the integral or differential operators accept non-integer orders. Addressing the challenges posed by nonlinear FIDEs is a recognized difficulty, necessitating the application of generic methods with immediate practical relevance. This work introduces the Universal Fractional Integro-Differential Equation Solvers (UniFIDES), a comprehensive machine learning platform designed to expeditiously solve a variety of FIDEs in both forward and inverse directions, without the need for ad hoc manipulation of the equations. The effectiveness of UniFIDES is demonstrated through a collection of integer-order and fractional problems in science and engineering. Our results highlight UniFIDES' ability to accurately solve a wide spectrum of integro-differential equations and offer the prospect of using machine learning platforms universally for discovering and describing dynamical and complex systems.

cs.LG↗

arXiv2023

Network physics of attractive colloidal gels: Resilience, Rigidity, and Phase Diagram

Attractive colloidal gels exhibit solid-like behavior at vanishingly small fractions of solids, owing to ramified space-spanning networks that form due to particle-particle interactions. These networks give the gel its rigidity, and as the attraction between the particles grows, so does the elasticity of the colloidal network formed. The emergence of this rigidity can be described through a mean field approach; nonetheless, fundamental understanding of how rigidity varies in gels of different attraction strengths is lacking. Moreover, recovering an accurate gelation phase diagram based on the system's variables have been an extremely challenging task. Understanding the nature of these fractal clusters, and how rigidity emerges from their connections is key to controlling and designing gels with desirable properties. Here, we employ well-established concepts of network science to interrogate and characterize the network of colloidal gels. We construct a particle-level network, having all the spatial coordinates of colloids with different attraction levels, and also identify polydisperse rigid fractal clusters using a Gaussian Mixture Model, to form a coarse-grained cluster network that distinctly shows main physical features of the colloidal gels. A simple mass-spring model then is used to recover quantitatively the elasticity of colloidal gels from these cluster networks. Interrogating the resilience of these gel networks show that the elasticity of a gel (a dynamic property) is directly correlated to its cluster network's resilience (a static measure). Finally, we use the resilience investigations to devise [and experimentally validate] a fully resolved phase diagram for colloidal gelation, with a clear solid-liquid phase boundary using a single volume fraction of particles well beyond this phase boundary.

cond-mat.soft↗

arXiv2022

The Mnemosyne Number and the Rheology of Remembrance

The concept of a Deborah number is widely used in study of viscoelastic materials and to represent the ratio of a material relaxation time to the timescale of observation, and to demarcate transitions between predominantly viscous or elastic material responses. However, this construct does not help quantify the importance of long transients and non-monotonic stress jumps that are often observed in more complex time-varying systems. Many of these non-intuitive effects are lumped collectively under the term thixotropy; however, no proper nouns are associated with the key phenomena observed in such materials. Thixotropy arises from the ability of a complex structured fluid to remember its prior deformation history, so it is natural to name the dimensionless group representing such behavior with respect to the ability to remember. In Greek mythology, Mnemosyne was mother of the nine Muses and the goddess of memory. We thus propose the definition of a Mnemosyne number as the dimensionless product of the thixotropic time scale and the imposed rate of deformation. The Mnemosyne number is thus a measure of the flow strength compared to the thixotropic timescale. Since long transients responses are endemic to thixotropic materials, one also needs to consider the duration of flow. The relevant dimensionless measure of this duration can be represented in terms of a mutation number which compares the timescale of experiment/observation to the thixotropic timescale. Collating the mutation number and the Mnemosyne number, we construct a general two-dimensional map of thixotropic behavior, and quantify these ideas using canonical thixotropic models.

cond-mat.soft↗

arXiv2020

Jamming Distance Dictates Colloidal Shear Thickening

We report experimental and computational observations of dynamic contact networks for colloidal suspensions undergoing shear thickening. The dense suspensions are comprised of sterically stabilized poly(methyl methacrylate) hard sphere colloids that are spherically symmetric and have varied surface roughness. Confocal rheometry and dissipative particle dynamics simulations show that the shear thickening strength scales exponentially with the scaled deficit contact number and the scaled jamming distance. Rough colloids, which experience additional tangential and rolling constraints, require an average of 1.5 - 2 fewer particle contacts as compared to smooth colloids, in order to generate the same shear thickening strength. This is because the surface roughness enhances geometric friction in a way that the rough colloids do not experience a large change in the free volume near the jamming point. In contrast, smooth colloids must undergo significant reduction in the free volume to support an equivalent shear stress. The available free volume for different colloid roughness is related to the deficiency from the maximum number of nearest neighbors at jamming under shear. Our results further suggest that the force per contact is different for particles with different morphologies.

cond-mat.soft↗

arXiv2019

Time-Rate-Transformation framework for targeted assembly of short-range attractive colloidal suspensions

The aggregation of attractive colloids has been extensively studied from both theoretical and experimental perspectives as the fraction of solid particles is changed, and the range, type and strength of attractive or repulsive forces between particles varies. The resulting gels consisting of disordered assemblies of attractive colloidal particles, have also been investigated with regards to percolation, phase separation, and the mechanical characteristics of the resulting fractal networks. Despite tremendous progress in our understanding of the gelation process, and the exploration of different routes for arresting the dynamics of attractive colloids, the complex interplay between convective transport processes and many-body effects in such systems has limited our ability to drive the system towards a specific configuration. Here we study a model attractive colloidal system over a wide range of particle characteristics and flow conditions undergoing aggregation far from equilibrium. The complex multiscale dynamics of the system can be understood using a Time-Rate-Transformation diagram adapted from understanding of materials processing in block copolymers, supercooled liquids and much stiffer glassy metals to direct targeted assembly of attractive colloidal particles.

cond-mat.soft↗

arXiv2016

Microstructural rearrangements and their rheological implications in a model Thixotropic Elasto-Visco-Plastic (TEVP) fluid

We identify the sequence of microstructural changes that characterize the evolution of an attractive particulate gel under flow and discuss their implications on macroscopic rheology. Dissipative Particle Dynamics (DPD) is used to monitor shear-driven evolution of a fabric tensor constructed from the ensemble spatial configuration of individual attractive constituents within the gel. By decomposing this tensor into isotropic and non-isotropic components we show that the average coordination number correlates directly with the flow curve of the shear stress vs. shear rate, consistent with theoretical predictions for attractive systems. We show that the evolution in non-isotropic local particle rearrangements are primarily responsible for stress overshoots (strain-hardening) at the inception of steady shear flow and also lead, at larger times and longer scales, to microstructural localization phenomena such as shear banding flow-induced structure formation in the vorticity direction.

cond-mat.soft↗