arXiv subjects

Karim Gadelrab

Publications and source records attributed to Karim Gadelrab.

6 recordsLinked to original sources

arXiv2026

Practical Insights to Thin Film Dewetting

Thin liquid films exhibit rich instability and rupture dynamics that critically impact coating performance across many applications. In this work, we use the lattice Boltzmann method (LBM) simulations within a lubrication-theory framework to systematically quantify how film thickness, surface energy, wettability, and intermolecular forces govern dewetting kinetics and long-time morphology. Master-curve scalings are identified for the time to dewet, revealing a strong power-law sensitivity to film thickness and a comparatively weak dependence on moderate variations in the contact angle. Following rupture, the film reaches a physically meaningful coverage plateau, whose magnitude correlates with material parameters and provides a practical window for morphological stabilization prior to coarsening. Long-time evolution obeys classical coarsening scaling laws, with surface energy controlling domain density. These results demonstrate that lubrication-based models can deliver predictive design guidance for evaluating coating robustness and forming materials and surface engineering strategies. Source code is available at https://github.com/Zitzeronion/Swalbe.jl.

cond-mat.mtrl-sci↗

arXiv2026

Mixture of Experts Framework in Machine Learning Interatomic Potentials for Atomistic Simulations

First-principles atomistic simulations are essential for understanding complex material phenomena but are fundamentally limited by their computational cost. While Machine Learning Interatomic Potentials (MLIPs) have drastically improved cost for a given accuracy, their inference cost remains a bottleneck for massive systems or long timescales. To address this, we introduce a multifidelity "Mixture-of-Experts" framework based on the E(3)-equivariant Allegro architecture. Our method spatially partitions the simulation domain into a chemically complex region (e.g., reactive interfaces) and a simple region (e.g., bulk lattice), assigning models of varying capacity to each. Among the challenges in such static domain decomposition, the mechanical mismatch between models at the interface is particularly critical, as it can generate artificial stress fields and instability. We address this challenge with a co-training strategy in which the loss function includes agreement constraints -- penalties on per-atom energy and force discrepancies between models evaluated on shared bulk environments -- forcing the independent models to learn a consistent physical description of the bulk material. We validate this approach on a realistic Pt+CO catalytic system, demonstrating that the co-trained models maintain exact energy conservation, align their bulk mechanical response (e.g., equation of state and bulk modulus), and achieve predictive accuracy comparable to a full high-fidelity simulation at more than twice the computational speed.

physics.comp-ph↗

arXiv2026

Atomistic modeling of the hygromechanical properties of amorphous Polyamide 6,6

Polyamide 6,6 (PA66) is a key engineering polymer, whose unique mechanical properties arise from strong interchain hydrogen bonding. However, its hygroscopic nature makes it highly sensitive to water uptake, which markedly alters its thermomechanical behavior. Contrary to traditional experimental approaches, this study uses atomistic molecular dynamics (MD) simulations to investigate the role of water in modifying the glass transition temperature (Tg) and the viscoelastic response of amorphous PA66. Simulations capture a nonmonotonic dependence of Tg on water content. At low water concentrations, isolated water molecules bind to amide groups and restrict chain mobility, while beyond ~2.5 wt %, water clustering disrupts the hydrogen bond network and causes a pronounced Tg depression. Analysis of amide group fluctuations reveals a master correlation between local segmental dynamics and bulk density, verifying the known temperature humidity equivalence in terms of density variation. The computed Young's modulus exhibits systematic softening with increasing temperature and water content, consistent with experimental trends, albeit a more pronounced impact of water at low temperatures. Time temperature superposition behavior is observed for both dry and hydrated systems. This work provides molecular scale information on the hygromechanical coupling in PA66 and demonstrates the ability of MD simulations to predict water induced transitions that govern the macroscopic behavior of polyamides.

cond-mat.mtrl-sci↗

arXiv2024

Polymer Composites Informatics for Flammability, Thermal, Mechanical and Electrical Property Predictions

Polymer composite performance depends significantly on the polymer matrix, additives, processing conditions, and measurement setups. Traditional physics-based optimization methods for these parameters can be slow, labor-intensive, and costly, as they require physical manufacturing and testing. Here, we introduce a first step in extending Polymer Informatics, an AI-based approach proven effective for neat polymer design, into the realm of polymer composites. We curate a comprehensive database of commercially available polymer composites, develop a scheme for machine-readable data representation, and train machine-learning models for 15 flame-resistant, mechanical, thermal, and electrical properties, validating them on entirely unseen data. Future advancements are planned to drive the AI-assisted design of functional and sustainable polymer composites.

cond-mat.soft↗

arXiv2020

Advances in dynamic AFM: from nanoscale energy dissipation to material properties in the nanoscale

Since the inception of the atomic force microscope AFM, dynamic methods have been very fruitful by establishing methods to quantify dissipative and conservative forces in the nanoscale and by providing a means to apply gentle forces to the samples with high resolution. Here we review developments that cover over a decade of our work on energy dissipation, phase contrast and the extraction of relevant material properties from observables. We describe the attempts to recover material properties via one dimensional amplitude and phase curves from force models and explore the evolution of these methods in terms of force reconstruction, fits of experimental measurements, and the more recent advances in multifrequency AFM.

cond-mat.mes-hall↗

arXiv2018

Self-assembly of cylinder forming diblock copolymers on modulated substrates: a simulation study

Self-consistent field theory (SCFT) and strong segregation theory (SST) are used to explore the parameter space governing the self-assembly of cylinder forming block copolymers (BCPs) on a modulated substrate. The stability of in-plane cylinders aligning parallel or perpendicular to substrate corrugation is investigated for different barrier height and spacing for a weakly preferential substrate. Within the conditions of our simulations, the results indicate that cylinder alignment orthogonal to substrate undulation is promoted at low barrier height when substrate is preferential to minority block, independent of barrier spacing. Commensurability is shown to play a limited role in the assembly of orthogonal meshes. Parallel alignment is readily achieved at larger barrier height, near condition of commensuration between barrier spacing and polymer equilibrium period. This is particularly true when substrate is attractive to majority block. The interplay between barrier shape and substrate affinity can be utilized in nanotechnology application such as mesh creation, density multiplication, and 3D BCP morphologies.

cond-mat.mtrl-sci↗