arXiv subjects

Elnaz Hajizadeh

Publications and source records attributed to Elnaz Hajizadeh.

12 recordsLinked to original sources

arXiv2026

Concentration-dependent shear response of multi-chain amphiphilic block copolymer self-assemblies

Amphiphilic block copolymers self-assemble into diverse nanoscale morphologies with significant implications for drug delivery. This work presents systematic Brownian dynamics simulations of multi-chain diblock and triblock copolymers across dilute and semi-dilute unentangled regimes, hydrophobic fractions, f of 0-1, and shear rates of 0-0.1 1/ns. In the dilute regime, quiescent conditions yield spherical micelles evolving to cigar-like structures at shear rate ~0.01 1/ns and fragmenting at higher shear; varying f produces dispersed chains (f=0), cigar-like (f=0.25), short cylindrical (f=0.5), and gnarled or worm-like (f=0.75) micelles, culminating in sheet-like phase-separated structures (f=1). While, in the semi-dilute regime, shear drives collective reorganisation toward sheet-like morphologies at moderate rates before fragmentation; the f-dependent progression yields cigar-like (f=0.25), sheet-like (f=0.5), and necklace micelles (f=0.75), with larger phase-separated domains at f=1. Rheological characterisation reveals a universal architectural inversion between equilibrium and flow conditions: diblocks show higher equilibrium viscosity while triblocks maintain superior viscosity under flow via bridging networks. Aggregation number scaling exponents of alpha=0.833 in dilute, consistent with star-to-crew-cut bounds of 0.8 to 1.0, and alpha=1.07 in semi-dilute confirm the concentration-driven transition between regimes. Viscoelastic analysis establishes universal non-terminal power-law scaling across all conditions, governed by micellar relaxation dynamics independent of concentration or topology. These findings provide valuable insights into tailoring the injectability and flow behaviour of block copolymers in drug delivery formulations.

cond-mat.soft↗

arXiv2026

Structure and rheology of multi-chain amphiphilic block copolymers under shear in dilute solutions

This study presents a computational investigation of self-assembly and rheological behaviour of multichain amphiphilic block copolymers under varying chain length, architecture, composition, and shear rate. Using Brownian dynamics (BD) simulations, we systematically examined bead-spring model multi-chain diblock and triblock copolymers with chain lengths of 12-48 beads, hydrophobic fractions (f) ranging from 0 to 1.0, and shear rates spanning 0-0.1 1/ns. In the dilute regime, results demonstrate that triblock copolymers form extensive 3D networks with bridging architectures through hydrophobic end blocks, achieving solution viscosities up to half an order of magnitude higher than diblock systems, with superior structural integrity under weak shear. At shear rate=0.003-0.01 1/ns, both chain architectures show increased gyration radius of individual chains within each micelle and decreased cluster counts, indicating aggregation of clusters prior to breakdown at higher shear rates. Shape anisotropy analysis reveals that triblocks develop highly elongated prolate structures (L1/L3 = 11) at high shear rates, while diblocks form more discrete micellar assemblies (L1/L3 = 7.5). Chain length analysis shows systematic increases in radius of gyration, with triblocks exhibiting an increase in cluster count, indicative of network percolation. Rheologically, triblock systems maintain lower crossover frequencies with increasing hydrophobic fraction, reflecting slower network relaxation versus diblocks. The terminal relaxation time of triblock copolymer systems increases with hydrophobic fraction due to double-ended hydrophobic bridging, while diblocks maintain stable values. These findings provide fundamental insights for the rational design of polymer-based drug carriers through architectural selection and flow conditions.

cond-mat.soft↗

arXiv2026

Machine Learning-integrated Multiscale Simulation Framework: Bridging Scales in Associative Polymer-Colloid Suspensions

Predicting the rheological behavior of associative polymers bridging colloidal particles into transient networks is fundamentally challenging because the coupled spatiotemporal scales prevent efficient molecular-fidelity modeling. We address this through a novel, unified multiscale simulation framework for telechelic polymer-colloid suspensions integrating: explicit-chain Brownian dynamics resolving polymer-particle association kinetics; active learning metamodels compressing kinetics into efficient surrogates; and Population Balance-Brownian Dynamics (Pop-BD) computing network-scale dynamics from metamodel predictions. Validated against explicit-chain Brownian dynamics, our framework accurately reproduces time-and frequency-dependent stress relaxation moduli, enabling simulations of larger systems over longer timescales. Systematic investigations reveal that network connectivity exhibits critical transitions at specific chain-to-particle ratios, with bond density and lifetime correlating to enhanced relaxation times and moduli. Higher particle volume fractions yield more persistent bonds and slower relaxation. This framework connects chain-level dynamics to macroscopic rheology, enabling computationally efficient rational design of associative colloidal materials for waterborne coatings and soft-matter applications.

cond-mat.soft↗

arXiv2026

StrAPS: Structural Angular Power Spectrum for Discovering Novel Morphologies in Block Copolymers

The morphologies of phase separating systems have formal distinctions such as symmetry groups, but the analysis protocol for labeling a particular phase field with a morphology requires manual expertise, arbitrary thresholds, or established signatures. In this work, it is investigated if the angular power spectrum of the 3D structure factor can discriminate between morphologies. The 3D structure factor is computed on configurations of phase separating block copolymers generated by coarse-grained molecular dynamics simulations. The shell of structure factor values containing the primary peaks is isolated. This 2D field on a sphere is decomposed into spherical harmonic modes of even polynomial degree $\ell\le 12$, then further reduced to the rotationally invariant angular power spectrum. It is found that these few coefficients for low $\ell$ discriminate robustly between different morphologies. This analysis serves as an automatic tool for flagging novel structures, without a need to enumerate the plausible morphologies in advance.

cond-mat.soft↗

arXiv2025

Molecular Dynamics Simulations of Membrane Selectivity of Star Peptides Across Different Bacterial and Mammalian Bilipids

Structurally nanoengineered antimicrobial peptide polymers (SNAPPs) are emerging as promising selective agents against bacterial membranes. In this study, we used all atom molecular dynamics simulation techniques to investigate the interaction of a promising cationic SNAPP architecture (Alt-SNAPP with 8 arms made of alternating lysine and valine residues) with modelled Gram-negative, Gram-positive, mammalian, and red blood cell membranes. Alt-SNAPP exhibited rapid and stable binding to bacterial membranes, driven by electrostatic interactions with anionic lipids such as phosphatidylglycerol (PG) and cardiolipin (CL), and supported by membrane fluidity. In contrast, mammalian and red blood cell membranes, enriched in zwitterionic lipids and cholesterol, resisted peptide association entirely. Analyses of center of mass distance, partial density, hydrogen bonding, and interaction energy confirmed that SNAPP remains fully excluded from host like membranes while forming stable, multivalent interactions with bacterial bilayers. These findings provide mechanistic insight into membrane selectivity of SNAPP and offer a molecular framework for designing next generation antimicrobial polymers with minimal off target toxicity.

q-bio.BM↗

arXiv2025

Interpretable SHAP-bounded Bayesian Optimization for Underwater Acoustic Metamaterial Coating Design

We developed an interpretability informed Bayesian optimization framework to optimize underwater acoustic coatings based on polyurethane elastomers with embedded metamaterial features. A data driven model was employed to analyze the relationship between acoustic performance, specifically sound absorption and the corresponding design variables. By leveraging SHapley Additive exPlanations (SHAP), a machine learning interpretability tool, we identified the key parameters influencing the objective function and gained insights into how these parameters affect sound absorption. The insights derived from the SHAP analysis were subsequently used to automatically refine the bounds of the optimization problem automatically, enabling a more targeted and efficient exploration of the design space. The proposed approach was applied to two polyurethane materials with distinct hardness levels, resulting in improved optimal solutions compared to those obtained without SHAP-informed guidance. Notably, these enhancements were achieved without increasing the number of simulation iterations. Our findings demonstrate the potential of SHAP to streamline optimization processes by uncovering hidden parameter relationships and guiding the search toward promising regions of the design space. This work underscores the effectiveness of combining interpretability techniques with Bayesian optimization for the efficient and cost-effective design of underwater acoustic metamaterials under strict computational constraints and can be generalized towards other materials and engineering optimization problems.

cs.LG↗

arXiv2025

Lipidation-induced bacterial cell membrane translocation of star-peptides

The rapid emergence of multidrug-resistant (MDR) bacteria demands development of novel and effective antimicrobial agents. Structurally Nanoengineered Antimicrobial Peptide Polymers (SNAPPs), characterized by their unique star-shaped architecture and potent multivalent interactions, represent a promising solution. This study leverages molecular dynamics simulations to investigate the impact of lipidation on SNAPPs' structural stability, membrane interactions, and antibacterial efficacy. We show that lipidation with hexanoic acid (C6), lauric acid (C12), and stearic acid (C18) enhances the α-helical stability of SNAPP arms, facilitating deeper insertion into the hydrophobic core of bacterial membranes. Among the variants, C12-SNAPP exhibits the most significant bilayer disruption, followed by C6-SNAPP, whereas the excessive hydrophobicity of C18-SNAPP leads to pronounced arm back-folding towards the core, reducing its effective interaction with the bilayer and limiting its bactericidal performance. Additionally, potential of mean force (PMF) analysis reveals that lipidation reduces the free energy barrier for translocation through the bilipid membrane compared to non-lipidated SNAPPs. These findings underscore the critical role of lipidation in optimizing SNAPPs for combating MDR pathogens. By fine-tuning lipid chain lengths, this study provides a framework for designing next-generation antimicrobial agents to address the global antibiotic resistance crisis, advancing modern therapeutic strategies.

physics.bio-ph↗

arXiv2024

Procedural Construction of Atomistic Polyurethane Block Copolymer Models for High Throughput Simulations

In this work, methods are presented to automatically generate a fully atomistic LAMMPS models of arbitrary linear multiblock polyurethane copolymers. The routine detailed here receives as parameters the number of repeat units per hard block, the number of units in a soft block, and the number of soft blocks per chain, as well as chemical formulae of three monomers which will form the hard component, soft component, and chain extender. A routine is detailed for converting the chemical structure of a free monomer to the urethane bonded repeat units in a polymer. The python package RadonPy is leveraged to assemble these units into blocks, and the blocks into copolymers. Care is taken in this work to ensure that plausible atomic charges are assigned to repeat units in different parts of the chain. The static structure factor is calculated for a variety of chemistries, and the results compared with wide angle x-ray scattering data from experiments with corresponding composition. The generated models reproduce the amorphous halo observed in the scattering data as well as some of the finer details. Structure factor calculations are decomposed into the partial structure factors to interrogate the structural properties of the two block types separately. Parametric surveys are carried out of the effects of various parameters, including temperature, soft block length, and block connectivity on the observed structure. The routine detailed here for constructing models is robust enough to be executed automatically in a high throughput workflow for material design and discovery.

cond-mat.mtrl-sci↗

arXiv2024

Bacterial cell death: Atomistic simulations reveal pore formation as a mode of action of structurally nano engineered star peptide polymers

Multidrug resistance (MDR) to conventional antibiotics is one of the most urgent global health threats, necessitating the development of effective and biocompatible antimicrobial agents that are less inclined to provoke resistance. Structurally Nanoengineered Antimicrobial Peptide Polymers (SNAPPs) are a novel and promising class of such alternatives. These star-shaped polymers are made of a dendritic core with multiple arms made of co-peptides with varying amino acid sequences. Through a comprehensive set of in vivo experiments, we (Nature Microbiology, 1, 16162, 2016) showed that SNAPPs with arms made of random blocks of lysine (K) and valine (V) residues exhibit sub-micron M efficacy against Gram-negative and Gram-positive bacteria tested. Cryo-TEM images suggested pore formation by SNAPP with random block co-peptide arms as one of their mode of actions. However, the molecular mechanisms responsible for this mode of action of SNAPP were not fully understood. To address this gap, we employed atomistic molecular dynamics simulation technique to investigate the influence of three different sequences of amino acids, namely 1) alternating block KKV 2) random block and 3) di-block motifs on secondary structure of their arms and SNAPP's overall configuration as well as their interactions with lipid bilayer. We, for the first time identified a step-by-step mechanism through which alternating block and random SNAPPs interact with lipid bilayer and leads to pore formation, hence cell death. These insights provide a strong foundation for further optimization of the chemical structure of SNAPPs for maximum performance against MDR bacteria, therefore offering a promising avenue for addressing antibiotic resistance and development of effective antibacterial agents.

cond-mat.soft↗

arXiv2022

A novel machine learning enabled hybrid optimization framework for efficient and transferable coarse-graining of a model polymer

This work presents a novel framework governing the development of an efficient, accurate, and transferable coarse-grained (CG) model of a polyether material. The proposed framework combines the two fundamentally different classical optimization approaches for the development of coarse-grained model parameters; namely bottom-up and top-down approaches. This is achieved through integrating the optimization algorithms into a machine learning (ML) model, trained using molecular dynamics (MD) simulation data. In the bottom-up approach, bonded interactions of the CG model are optimized using deep neural networks (DNN), where atomistic bonded distributions are matched. The atomistic distributions emulate the local chain structure. In the top-down approach, optimization of nonbonded potentials is accomplished by reproducing the temperature-dependent experimental density. We demonstrate that CG model parameters achieved through our machine-learning enabled hybrid optimization framework fulfills the thermodynamic consistency and transferability issues associated with the classical approaches to coarse-graining model polymers. We demonstrate the efficiency, accuracy, and transferability of the developed CG model, using our novel framework through accurate predictions of chain size as well as chain dynamics, including the limiting behavior of the glass transition temperature, diffusion, and stress relaxation spectrum, where none were included in the potential parameterization process. The accuracy of the predicted properties are evaluated in the context of molecular theories and available experimental data.

physics.chem-ph↗

arXiv2022

A machine learning accelerated inverse design of underwater acoustic polyurethane coatings with cylindrical voids

Here, we report the development of a detailed "Materials Informatics" framework for the design of acoustic coatings for underwater sound attenuation through integrating Machine Learning (ML) and statistical optimization algorithms with a Finite Element Model (FEM). The finite element models were developed to simulate the realistic performance of the acoustic coatings based on polyurethane (PU) elastomers with embedded cylindrical voids. The FEM results revealed that the frequency-dependent viscoelastic behavior of the polyurethane matrix has a significant impact on the magnitude and frequency of the absorption peak associated with the cylinders at low frequencies, which has been commonly ignored in previous studies on similar systems. The data generated from the FEM was used to train a Deep Neural Network (DNN) to accelerate the design process, and subsequently, was integrated with a Genetic Algorithm (GA) to determine the optimal geometric parameters of the cylinders to achieve maximized, broadband, low-frequency waterborne sound attenuation. A significant, broadband, low-frequency attenuation is achieved by optimally configuring the layers of cylindrical voids and using attenuation mechanisms, including Fabry-Pérot resonance and Bragg scattering of the layers of voids. Integration of the machine learning technique into the optimization algorithm further accelerated the exploration of the high dimensional design space for the targeted performance. The developed DNN exhibited significantly increased speed (by a factor of $4.5\times 10^3$ ) in predicting the absorption coefficient compared to the conventional FEM(s). Therefore, the acceleration brought by the materials informatics framework brings a paradigm shift to the design and development of acoustic coatings compared to the conventional trial-and-error practices.

eess.SP↗

arXiv2022

Molecular dynamics study of the linear viscoelastic shear and bulk relaxation moduli of poly(tetramethylene oxide) (PTMO)

Here we report the linear viscoelastic properties of amorphous poly(tetramethylene oxide) (PTMO), which is one of the key components in synthesizing segmented polyurethane (PU) elastomers. The temperature and molecular weight dependent viscoelastic behavior is investigated in detail by computing the shear relaxation modulus G(t) and the bulk relaxation modulus K(t), using the Green-Kubo relationship with correlation function. Our results provide new data for PTMO melt from the united atom model and also extend the existing knowledge of viscoelastic properties of polymers in general. The predicted viscoelastic behavior range is shifted on a master curve using the time-temperature superposition principle (TTSP) with horizontal and vertical shift factors. The emerging shift factors agree with the Williams-Landel-Ferry (WLF) equation. For the validation of the united-atom model of PTMO using the TraPPE-UA force field we explored the transport properties and observed a position-dependent diffusion dynamics throughout the polymer chain, which subsequently influences the scaling laws for chain dynamics. These findings are discussed in terms of emerging experimental evidence on position dependent displacement for different chain portions along the chain length.

cond-mat.soft↗