arXiv subjects

Bamin Khomami

Publications and source records attributed to Bamin Khomami.

3 recordsLinked to original sources

arXiv2025

ClusTEK: A grid clustering algorithm augmented with diffusion imputation and origin-constrained connected-component analysis: Application to polymer crystallization

Grid clustering algorithms are valued for their efficiency in large-scale data analysis but face persistent limitations: parameter sensitivity, loss of structural detail at coarse resolutions, and misclassifications of edge or bridge cells at fine resolutions. Previous studies have addressed these challenges through adaptive grids, parameter tuning, or hybrid integration with other clustering methods, each of which offers limited robustness. This paper introduces a grid clustering framework that integrates Laplacian-kernel diffusion imputation and origin-constrained connected-component analysis (OC-CCA) on a uniform grid to reconstruct the cluster topology with high accuracy and computational efficiency. During grid construction, an automated preprocessing stage provides data-driven estimates of cell size and density thresholds. The diffusion step then mitigates sparsity and reconstructs missing edge cells without over-smoothing physical gradients, while OC-CCA constrains component growth to physically consistent origins, reducing false merges across narrow gaps. Operating on a fixed-resolution grid with spatial indexing ensures the scaling of O(nlog n). Experiments on synthetic benchmarks and polymer simulation datasets demonstrate that the method correctly manages edges, preserves cluster topology, and avoids spurious connections. Benchmarking on polymer systems across scales (9k, 180k, and 989k atoms) shows that optimal preprocessing, combined with diffusion-based clustering, reproduces atomic-level accuracy and captures physically meaningful morphologies while delivering accelerated computation.

physics.comp-ph↗

arXiv2025

Machine Learning Workflow for Analysis of High-Dimensional Order Parameter Space: A Case Study of Polymer Crystallization from Molecular Dynamics Simulations

Currently, identification of crystallization pathways in polymers is being carried out using molecular simulation-based data on a preset cut-off point on a single order parameter (OP) to define nucleated or crystallized regions. Aside from sensitivity to cut-off, each of these OPs introduces its own systematic biases. In this study, an integrated machine learning workflow is presented to accurately quantify crystallinity in polymeric systems using atomistic molecular dynamics data. Each atom is represented by a high-dimensional feature vector that combines geometric, thermodynamic-like, and symmetry-based descriptors. Low dimensional embeddings are employed to expose latent structural fingerprints within atomic environments. Subsequently, unsupervised clustering on the embeddings identified crystalline and amorphous atoms with high fidelity. After generating high quality labels with multidimensional data, we use supervised learning techniques to identify a minimal set of order parameters that can fully capture this label. Various tests were conducted to reduce the feature set, demonstrating that using only three order parameters is sufficient to recreate the crystallization labels. Based on these observed OPs, the crystallinity index (C-index) is defined as the logistic regression model's probability of crystallinity, remaining bimodal throughout the process and achieving over 0.98 classification performance (AUC). Notably, a model trained on one or a few snapshots enables efficient on-the-fly computation of crystallinity. Lastly, we demonstrate how the optimal C-index fit evolves during various stages of crystallization, supporting the hypothesis that entropy dominates early nucleation, while symmetry gains relevance later. This workflow provides a data-driven strategy for OP selection and a metric to monitor structural transformations in large-scale polymer simulations.

physics.comp-ph↗

arXiv2007

Self-similar shear-thickening behavior in CTAB/NaSal surfactant solutions

The effect of salt concentration Cs on the critical shear rate required for the onset of shear thickening and apparent relaxation time of the shear-thickened phase, has been investigated systematically for dilute CTAB/NaSal solutions. Experimental data suggest a self-similar behavior of the critical shear rate and relaxation time as functions of Cs. Specifically, the former ~ Cs^(-6) whereas the latter ~ Cs^(6) such that an effective Weissenberg number for the onset of the shear thickened phase is only weakly dependent on Cs. A procedure has been developed to collapse the apparent shear viscosity versus shear rate data obtained for various values of Cs into a single master curve. The effect of Cs on the elastic modulus and mesh size of the shear-induced gel phase for different surfactant concentrations is discussed. Experiments performed using different flow cells (Couette and cone-and-plate) show that the critical shear rate, relaxation time and the maximum viscosity attained are geometry-independent. The elastic modulus of the gel phase inferred indirectly by employing simplified hydrodynamic instability analysis of a sheared gel-fluid interface is in qualitative agreement with that predicted for an entangled phase of living polymers. A qualitative mechanism that combines the effect of Cs on average micelle length and Debye parameter with shear-induced configurational changes of rod-like micelles is proposed to rationalize the self-similarity of SIS formation.

physics.flu-dyn↗