arXiv subjects

Ashwini Malviya

Publications and source records attributed to Ashwini Malviya.

4 recordsLinked to original sources

arXiv2026

Nematic Wigner crystals in rhombohedral multilayer graphene

Recent experiments have reported evidence for Wigner crystals (WCs) in rhombohedral graphene. Here, we investigate Wigner crystallization in rhombohedral tetralayer graphene using projected Hartree-Fock (HF) calculations and time-dependent Hartree-Fock (TDHF) calculations. We first perform HF calculations with one electron per Wigner unit cell, and find nematic WCs (nWCs) that spontaneously break the threefold rotational symmetry $C_3$ and $C_3$-invariant WCs. In particular, there are two nWC regions in the phase diagram: one larger region at large displacement fields and low electron densities, and another smaller region at intermediate fields and high densities. Both the nWCs and the $C_3$-invariant WCs are valley-polarized states with zero Chern number, and have positive indirect gaps in the HF band structure. We then perform TDHF calculations to further test the local stability of the WC states. We find that all $C_3$-invariant WCs and half of the nWCs are locally stable, while the remaining nWCs are unstable towards WCs with two electrons per unit cell or metallic states. The predicted stable nWC phase can be identified experimentally by scanning tunneling microscopy through its anisotropic charge distribution or by angle-resolved transport measurements via a direction-dependent depinning voltage.

cond-mat.str-el↗

arXiv2025

EIE calculation and Collisional-Radiative modeling for Na-like Kr and Xe

As an extension to our previous work [1], a comprehensive theoretical study for Na-like Krypton and Xenon is carried out. Using MCDHF (Multiconfiguration Dirac-Hartee-Fock) along with RDW (Relativistic distorted wave) theory we calculate key atomic properties, electron-impact excitation (EIE), rate coefficients, and collision strength for these ions. We use these parameters to build a Collisional-Radiative model for Na-like Krypton and Xenon. For Na-like Krypton we compare our computed excitation energy, EIE cross-sections, rate coefficients, emission line intensity with previous work. Additionally we investigate variation of line ratios with temperature. For Na-like Xenon we compared excitation energy for various fine-structure with NIST( [2]) database and then provide our computed results for EIE cross-section,intensity profile, and the temperature dependence of line ratios for Na-like Xenon. Our findings offer atomic data for studies related Na-like ions.

physics.atom-ph↗

arXiv2025

Predicting Plasma Temperature From Line Intensities Using ML Models

In this work, ML models were used to predict the plasma temperature using the dataset obtained by implementing the CR-model for Na-like Krypton. The models included in the study are: Linear Regression, Lasso Regression, Support Vector Regression, Decision Trees, Random Forest, XGBoost, Multi-layer Perceptron and Convolutional Neural Network. For evaluating the models we used Mean Absolute Error, Mean Squared Error and R^2 Score as metrics, In our study Random Forest performed best as compared to other model considered, the study conclude that complex relation between the line-intensities and Plasma temperature can be capture by ML models and they can be used to predict the temperature with high accuracy.

physics.plasm-ph↗

arXiv2024

Comparison of Machine Learning Approaches for Classifying Spinodal Events

In this work, we compare the performance of deep learning models for classifying the spinodal dataset. We evaluate state-of-the-art models (MobileViT, NAT, EfficientNet, CNN), alongside several ensemble models (majority voting, AdaBoost). Additionally, we explore the dataset in a transformed color space. Our findings show that NAT and MobileViT outperform other models, achieving the highest metrics-accuracy, AUC, and F1 score on both training and testing data (NAT: 94.65, 0.98, 0.94; MobileViT: 94.20, 0.98, 0.94), surpassing the earlier CNN model (88.44, 0.95, 0.88). We also discuss failure cases for the top performing models.

cs.LG↗