arXiv subjects

Vidushi Sharma

Publications and source records attributed to Vidushi Sharma.

At least 19 recordsLinked to original sources

arXiv2026

Episode-wise spectro-polarimetry of GRB 220107A: Testing the hypothesis of evolving radiation mechanisms

We investigate the spectro-polarimetric properties of the long-duration GRB~220107A, which exhibited two distinct emission episodes separated by a 40 s quiescent gap, to test whether such multi-episode bursts show evidence for evolution in their underlying radiation mechanisms. We analyzed prompt emission data from AstroSat/CZTI, Fermi/GBM, and Konus-Wind, performing spectro-polarimetric analysis for each emission episode. The time-integrated polarization analysis shows no significant detection (PF$ < 38 \%$, $2\sigma$). Time-resolved analysis reveals clear spectral evolution between the two episodes, with episode 1 exhibiting a hard low-energy photon index and episode 2 showing substantial spectral softening ($\alpha \sim -0.72$). Regarding polarization: Episode 1 shows a low polarization upper limit (< 52\%), consistent with expectations for photospheric emission dominated by quasi-thermal Comptonization in a baryon-rich outflow. Episode 2 also shows overall low polarization (PF$ < 55 \%$, $2\sigma$), though sliding-window analysis yields a marginally elevated signal (PF$= 70 \pm 30\%$, BF = 2.8) between T0+76 to T0+88 s. The robust spectral softening between episodes could arise from sub-photospheric dissipation, optically thin synchrotron radiation in small-scale magnetic fields, or if the tentative polarization enhancement proves intrinsic, it would favor synchrotron emission in large-scale ordered magnetic fields. The spectral evolution of GRB 220107A, combined with our polarimetric constraints, demonstrates the diagnostic potential of time-resolved spectro-polarimetry for constraining GRB prompt emission physics. We present GRB 220107A as a test case illustrating how future higher sensitivity observations could discriminate between competing emission models for multi-episode bursts. Our results emphasize both the promise and current limitations of prompt phase polarimetry.

astro-ph.HE↗

arXiv2025

Large Language Model-driven Analysis of General Coordinates Network (GCN) Circulars

The General Coordinates Network (GCN) is NASA's time-domain and multimessenger alert system. GCN distributes two data products: automated "Notices" and human-generated "Circulars" that report the observations of high-energy and multimessenger astronomical transients. The flexible and nonstructured format of GCN Circulars, comprising more than 40,500 Circulars accumulated over three decades, makes it challenging to manually extract observational information, such as redshift or observed wave bands. In this work, we employ large language models (LLMs) to facilitate the automated parsing of transient reports. We develop a neural topic modeling pipeline with open-source tools for the automatic clustering and summarization of astrophysical topics in the Circulars archive. Using neural topic modeling and contrastive fine-tuning, we classify Circulars based on their observation wave bands and messengers. Additionally, we separate gravitational-wave event clusters and their electromagnetic counterparts from the Circulars archive. Finally, using the open-source Mistral model, we implement a system to automatically extract gamma-ray burst (GRB) redshift information from the Circulars archive, without the need for any training. Evaluation against the manually curated Neil Gehrels Swift Observatory GRB table shows that our simple system, with the help of prompt-tuning, output parsing, and retrieval augmented generation (RAG), can achieve an accuracy of 97.2% for redshift-containing Circulars. Our neural search-enhanced RAG pipeline accurately retrieved 96.8% of redshift Circulars from the manually curated archive. Our study demonstrates the potential of LLMs to automate and enhance astronomical text mining and provides a foundational work for future advances in transient alert analysis.

astro-ph.HE↗

arXiv2025

Mixed Stochastic-Deterministic Density Functional Theoretic Decomposition of Kubo-Greenwood Conductivities in the Projector Augmented Wave Formalism

Pairing the accuracy of Kohn-Sham density-functional framework with the efficiency of a stochastic algorithmic approach, mixed stochastic-deterministic Density Functional Theory (mDFT) achieves a favorable computational scaling with system sizes and electronic temperatures. We employ the recently developed mDFT formalism to investigate the dynamic charge-transport properties of systems in the warm dense matter regime. The optical conductivity spectra are computed for single- and multi- component mixtures of carbon, hydrogen, and beryllium using two complementary approaches: Kubo-Greenwood in the mDFT picture and real-time Time-Dependent mDFT. We further devise a decomposition of the Onsager coefficients leading up to the Kubo-Greenwood spectra to exhibit contributions from the deterministic, stochastic, and mixed electronic state transitions at different incident photon energies.

physics.chem-ph↗

arXiv2025

Radiation-Hydrodynamics Effects in an Inhomogeneous Medium

Radiation flow through an inhomogeneous medium is critical in a wide range of physics and astronomy applications from transport across cloud layers on the earth to the propagation of supernova blast-waves producing UV and X-ray emission in supernovae. Radiation interacts with matter driving hydrodynamic feedback that further alters the radiation characteristics (energy and angular distribution). This paper reviews the current state of the art in the modeling of inhomogeneous radiation transport, subgrid models developed to capture this often-unresolved physics, and the experiments designed to improve our understanding of these models. This paper focuses on simulations based on upcoming experiments designed to test this physics. We present a series of detailed simulations (both single-clump and multi-clump conditions) probing the dependence on the physical properties of the radiation front (e.g. radiation energy) and material characteristics (specific heat, opacity, clump densities). We find that, unless the radiation pressure is high, the clumps will heat and then expand, effectively cutting off the radiation flow. The expanding winds can also produce shocks that generates high energy emission. We compare our detailed simulations with some of the current subgrid prescriptions, identifying some of the limitations of these current models.

astro-ph.IM↗

arXiv2025

Chemical Foundation Model Guided Design of High Ionic Conductivity Electrolyte Formulations

Designing optimal formulations is a major challenge in developing electrolytes for the next generation of rechargeable batteries due to the vast combinatorial design space and complex interplay between multiple constituents. Machine learning (ML) offers a powerful tool to uncover underlying chemical design rules and accelerate the process of formulation discovery. In this work, we present an approach to design new formulations that can achieve target performance, using a generalizable chemical foundation model. The chemical foundation model is fine-tuned on an experimental dataset of 13,666 ionic conductivity values curated from the lithium-ion battery literature. The fine-tuned model is used to discover 7 novel high conductivity electrolyte formulations through generative screening, improving the conductivity of LiFSI and LiDFOB based electrolytes by 82% and 172%, respectively. These findings highlight a generalizable workflow that is highly adaptable to the discovery of chemical mixtures with tailored properties to address challenges in energy storage and beyond.

cond-mat.mtrl-sci↗

arXiv2025

Scaling active spaces in simulations of surface reactions through sample-based quantum diagonalization

Quantum-chemical simulations are essential for predicting energies of chemical reactions. Accurately solving the many-body Schr\"odinger equation for reagent and product states of most relevant chemical process is, however, unfeasible. Quantum computing offers a pathway for predicting energies of correlated electronic systems with localized interactions. Here, we apply a quantum embedding approach for investigating oxygen reduction reactions at the electrode surface in Lithium batteries, a representative example of energetic analysis in localized chemical reactions. We employ an Active Space Selection method based on Density Difference Analysis for identifying the orbitals involved in the reaction. Leveraging the Local Unitary Cluster Jastrow ansatz for state preparation, the active-space orbitals are then processed on a quantum computer. As quantum algorithms, we use Sample-based Quantum Diagonalization, SQD, and its extended version, Ext-SQD, which integrates electronic excitations into the quantum-selected electronic configuration subspace. The largest configurations are represented by quantum circuits mapped onto 80 qubits of an IBM Heron R2 quantum processing unit. For up to 12 orbitals, we are able to benchmark the quantum-computed reaction energies against results obtained with Complete Active Space Configuration Interaction. For benchmarking results in active spaces as large as 32 orbitals, we resort to Heat-Bath Configuration Interaction and Coupled Cluster Singles and Doubles calculations, respectively. At 27 orbitals, the Ext-SQD results exhibit prediction accuracy improvements with regard to the standard, quantum-chemical reference methods that remain computationally feasible at that scale. The results indicate the potential of sample-based quantum diagonalization for performing high-accuracy reaction modeling in chemistry and materials science.

quant-ph↗

arXiv2025

Group Conductivity and Nonadiabatic Born Effective Charges of Disordered Metals, Warm Dense Matter, and Hot Dense Plasma

The average ionization state is a critical parameter in plasma models for charged particle transport, equation of state, and optical response. The dynamical or nonadiabatic Born effective charge (NBEC), calculated via first principles time-dependent density functional theory, provides exact ionic partitioning of bulk electron response for both metallic and insulating materials. The NBEC can be trivially transformed into a ''group conductivity," that is, the electron conductivity ascribed to a subset of ions. We show that for disordered metallic systems, such as warm dense matter (WDM) and hot dense plasma, the static limit of the NBEC is different from the average ionization state, but that the ionization state can be extracted from the group conductivity even in mixed systems. We demonstrate this approach using a set of archetypical examples, including cold and warm aluminium, low- and high- density WDM carbon, and a WDM carbon-beryllium-hydrogen mixture.

cond-mat.mtrl-sci↗

arXiv2024

Improving Electrolyte Performance for Target Cathode Loading Using Interpretable Data-Driven Approach

Higher loading of active electrode materials is desired in batteries, especially those based on conversion reactions, for enhanced energy density and cost efficiency. However, increasing active material loading in electrodes can cause significant performance depreciation due to internal resistance, shuttling, and parasitic side reactions, which can be alleviated to a certain extent by a compatible design of electrolytes. In this work, a data-driven approach is leveraged to find a high-performing electrolyte formulation for a novel interhalogen battery custom to the target cathode loading. An electrolyte design consisting of 4 solvents and 4 salts is experimentally devised for a novel interhalogen battery based on a multi-electron redox reaction. The experimental dataset with variable electrolyte compositions and active cathode loading, is used to train a graph-based deep learning model mapping changing variables in the battery's material design to its specific capacity. The trained model is used to further optimize the electrolyte formulation compositions for enhancing the battery capacity at a target cathode loading by a two-fold approach: large-scale screening and interpreting electrolyte design principles for different cathode loadings. The data-driven approach is demonstrated to bring about an additional 20% increment in the specific capacity of the battery over capacities obtained from the experimental optimization.

cs.LG↗

arXiv2024

Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model

Development of efficient and high-performing electrolytes is crucial for advancing energy storage technologies, particularly in batteries. Predicting the performance of battery electrolytes rely on complex interactions between the individual constituents. Consequently, a strategy that adeptly captures these relationships and forms a robust representation of the formulation is essential for integrating with machine learning models to predict properties accurately. In this paper, we introduce a novel approach leveraging a transformer-based molecular representation model to effectively and efficiently capture the representation of electrolyte formulations. The performance of the proposed approach is evaluated on two battery property prediction tasks and the results show superior performance compared to the state-of-the-art methods.

cs.LG↗

arXiv2024

Optical and Transport Properties of Plasma Mixtures from Ab Initio Molecular Dynamics

Predicting the charged particle transport properties of warm dense matter / hot dense plasma mixtures is a challenge for analytical models. High accuracy ab initio methods are more computationally expensive, but can provide critical insight by explicitly simulating mixtures. In this work, we investigate the transport properties and optical response of warm dense carbon-hydrogen mixtures at varying concentrations under either conserved electronic pressure or mass density at a constant temperature. We compare options for mixing the calculated pure species properties to estimate the results of the mixtures. We find that a combination of the Drude model with the Matthiessen's rule works well for DC electron transport and low frequency optical response. This breaks down at higher frequencies, where a volumetric mix of pure-species AC conductivities works better.

physics.plasm-ph↗

arXiv2023

Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance

Advanced computational methods are being actively sought for addressing the challenges associated with discovery and development of new combinatorial material such as formulations. A widely adopted approach involves domain informed high-throughput screening of individual components that can be combined into a formulation. This manages to accelerate the discovery of new compounds for a target application but still leave the process of identifying the right 'formulation' from the shortlisted chemical space largely a laboratory experiment-driven process. We report a deep learning model, Formulation Graph Convolution Network (F-GCN), that can map structure-composition relationship of the individual components to the property of liquid formulation as whole. Multiple GCNs are assembled in parallel that featurize formulation constituents domain-intuitively on the fly. The resulting molecular descriptors are scaled based on respective constituent's molar percentage in the formulation, followed by formalizing into a combined descriptor that represents a complete formulation to an external learning architecture. The use case of proposed formulation learning model is demonstrated for battery electrolytes by training and testing it on two exemplary datasets representing electrolyte formulations vs battery performance -- one dataset is sourced from literature about Li/Cu half-cells, while the other is obtained by lab-experiments related to lithium-iodide full-cell chemistry. The model is shown to predict the performance metrics like Coulombic Efficiency (CE) and specific capacity of new electrolyte formulations with lowest reported errors. The best performing F-GCN model uses molecular descriptors derived from molecular graphs that are informed with HOMO-LUMO and electric moment properties of the molecules using a knowledge transfer technique.

cond-mat.mtrl-sci↗

arXiv2023

Effect of Graphene Interface on Potassiation in a Graphene- Selenium Heterostructure Cathode for Potassium-ion Batteries

Selenium (Se) cathodes are an exciting emerging high energy density storage system for Potassium ion batteries(KIB), where potassiation reactions are less understood. Here, we present an atomic-level investigation of KxSe cathode enclosed in hexagonal lattices of carbon(C) characteristic of multilayered graphene matrix and multiwalled carbon nanotubes (MW-CNTs). Microstructural changes directed by graphene substrate in KxSe cathode are contrasted with graphene-free cathode. Graphene's binding affinity for long-chain polyselenides (Se-Se-Se = -2.82 eV and Se-Se = -2.646 eV) and ability to induce reactivity between Se and K are investigated. Furthermore, intercalation voltage for graphene enclosed KxSe cathode reaction intermediates are calculated with K2Se as the final discharged product. Our results indicate a single-step reaction near a voltage of 1.55 V between K and Se cathode. Our findings suggest that operating at higher voltages (~2V) could result in the formation of reaction intermediates where intercalation/deintercalation of K could be a challenge, and therefore cause irreversible capacity losses in the battery. Primary issues are the high binding energy of long-chain polyselenides with graphene that discourage K storage and Se-Se bond dissociation at low K concentrations. A comparison with graphene-free cathode highlights the substantial changes a van der Waals (vdW) graphene interface can bring in atomic-structure and electrochemistry of the KxSe cathode.

physics.app-ph↗

arXiv2023

Transferable and Robust Machine Learning Model for Predicting Stability of Si Anodes for Multivalent Cation Batteries

Data-driven methodology has become a key tool in computationally predicting material properties. Currently, these techniques are priced high due to computational requirements for generating sufficient training data for high-precision machine learning models. In this study, we present a Support Vector Regression (SVR)-based machine learning model to predict the stability of silicon (Si) - alkaline metal alloys, with a strong emphasis on the transferability of the model to new silicon alloys with different electronic configurations and structures. We elaborate on the role of the structural descriptor in imparting transferability to the model that is trained on limited data (~750 Si alloys) derived from the Material Project database. Three popular descriptors, namely X-Ray Diffraction (XRD), Sine Coulomb Matrix (SCM), and Orbital Field Matrix (OFM), are evaluated for representing Si alloys. The material structures are represented by descriptors in the SVR model, coupled with hyperparameter tuning techniques like Grid Search CV and Bayesian Optimization (BO), to find the best performing model for predicting total energy, formation energy and packing fraction of the Si alloy systems. The models are trained on Si alloys with lithium (Li), sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), and aluminum (Al) metals, where Si-Na and Si-Al systems are used as test structures. Our results show that XRD, an experimentally derived characterization of structures, performs most reliably as a descriptor for total energy prediction of new Si alloys. The study demonstrates that by qualitatively selection of training data, using hyperparameter tuning methods, and employing appropriate structural descriptors, the data requirements for robust and accurate ML models can be reduced.

cond-mat.mtrl-sci↗

arXiv2023

Stochastic and Mixed Density Functional Theory within the projector augmented wave formalism for the simulation of warm dense matter

Stochastic and mixed stochastic-deterministic density functional theory (DFT) are promising new approaches for the calculation of the equation-of-state and transport properties in materials under extreme conditions. In the intermediate warm dense matter regime, a state between correlated condensed matter and kinetic plasma, electrons can range from being highly localized around nuclei to delocalized over the whole simulation cell. The plane-wave basis pseudo-potential approach is thus the typical tool of choice for modeling such systems at the DFT level. Unfortunately, the stochastic DFT methods scale as the square of the maximum plane-wave energy in this basis. To reduce the effect of this scaling, and improve the overall description of the electrons within the pseudo-potential approximation, we present stochastic and mixed DFT developed and implemented within the projector augmented wave formalism. We compare results between the different DFT approaches for both single-point and molecular dynamics trajectories and present calculations of self-diffusion coefficients of solid density carbon from 1 to 50 eV.

physics.comp-ph↗

arXiv2022

Hard X-ray polarization catalog for a 5-year sample of Gamma-Ray Bursts using AstroSat CZT-Imager

Cadmium Zinc Telluride Imager (CZTI) aboard AstroSat has been regularly detecting Gamma-Ray Bursts (GRBs) since its launch in 2015. Its sensitivity to polarization measurements at energies above 100 keV allows CZTI to attempt spectro-polarimetric studies of GRBs. Here, we present the first catalog of GRB polarization measurements made by CZTI during its first five years of operation. This presents the time integrated polarization measurements of the prompt emission of 20 GRBs in the energy range 100-600 keV. The sample includes the bright GRBs which were detected within an angle range of 0-60 degree and 120-180 degree where the instrument has useful polarization sensitivity and is less prone to systematics. We implement a few new modifications in the analysis to enhance polarimetric sensitivity of the instrument. Majority of the GRBs in the sample are found to possess less / null polarization across the total bursts' duration in contrast to a small fraction of five GRBs exhibiting high polarization. The low polarization across the bursts can be speculated to be either due to the burst being intrinsically weakly polarized or due to varying polarization angle within the burst even when it is highly polarized. In comparison to POLAR measurements, CZTI has detected a larger number of cases with high polarization. This may be a consequence of the higher energy window of CZTI observations which results in the sampling of smaller duration of burst emissions in contrast to POLAR, thereby, probing emissions of less temporal variations of polarization properties.

astro-ph.HE↗

arXiv2022

Turbostratic Orientations, Water Confinement and Ductile-Brittle Fracture in Bi-layer Graphene

Bi-layer graphene (BLG) can be a cheaper and more stable alternative to graphene in several applications. With its mechanical strength being almost equivalent to graphene, BLG also brings advanced electronic and optical properties to the table. Furthermore, entrapment of water in graphene-based nano-channels and devices has been a recent point of interest for several applications ranging from energy to bio-physics. Therefore, it is crucial to study the over-all mechanical strength of such structures in order to prevent system failures in future applications. In the present work, Molecular Dynamics simulations have been used to study crack propagation in BLG with different orientations between the layers. There is a major thrust in analyzing how the angular orientation between the layers affect the horizontal and vertical crack propagation in individual layers of graphene. The study has been extended to BLG with confined water in interfaces. Over-all strength of graphene sheets when in contact with water content has been determined, and prominent regional conditions for crack initiation are pointed out. It was seen that in the presence of water content, graphene deviated from its characteristic brittle failure and exhibited the ductile fracture mechanism. Origin of cracks in graphenes was located at the region where the density of water dropped near the graphene surface, suggesting that the presence of hydroxyl groups decelerate the crack formation and propagation in straining graphenes.

physics.app-ph↗

arXiv2022

Developing Potential Energy Surfaces for Graphene-based 2D-3D Interfaces from Modified High Dimensional Neural Networks for Applications in Energy Storage

Mixed-dimensional heterostructures composed of two-dimensional (2D) and three-dimensional (3D) materials are undisputed next-generation materials for engineered devices due to their changeable properties. The present work computationally investigates the interface between 2D graphene and 3D tin (Sn) systems with density functional theory (DFT) method. It uses computationally demanding simulation data to develop machine learning (ML) based potential energy surfaces (PES). The approach to developing PES for complex interface systems in the light of limited data and transferability of such models has been discussed. To develop PES for graphene-tin interface systems, high dimensional neural networks (HDNN) are used that rely on atom-centered symmetry function to represent structural information. HDNN are modified to train on the total energies of the interface system rather than atomic energies. The performance of modified HDNN trained on 5789 interface structures of graphene|Sn is tested on new interfaces of the same material pair with varying levels of structural deviations from the training dataset. Root mean squared error (RMSE) for test interfaces fall in the range of 0.01-0.45 eV/atom, depending on the structural deviations from the reference training dataset. By avoiding incorrect decomposition of total energy into atomic energies, modified HDNN model is shown to obtain higher accuracy and transferability despite limited dataset. Improved accuracy in ML-based modeling approach promises cost-effective means of designing interfaces in heterostructure energy storage systems with higher cycle life and stability.

cond-mat.mtrl-sci↗

arXiv2022

Onset of particle acceleration during the prompt phase in gamma-ray bursts as revealed by synchrotron emission in GRB160821A

The physical processes of the gamma-ray emission and particle acceleration during the prompt phase in GRBs are still unsettled. In order to perform an unambiguous physical modelling of observations, a clear identification of the emission mechanism is needed. An instance of a clear identification is the synchrotron emission during the very strong flare in GRB160821A, that occurs during the prompt phase at 135 s. Here we show that the distribution of the radiating electrons in this flare is initially very narrow, but later develops a power-law tail of accelerated electrons. We thus identify for the first time the onset of particle acceleration in a GRB jet. The flare is consistent with a late energy release from the central engine causing an external-shock as it encounters a preexisting ring nebula of a progenitor Wolf-Rayet star. Relativistic forward and reverse shocks develop, leading to two distinct emission zones with similar properties. The particle acceleration only occurs in the forward shock, moving into the dense nebula matter. Here, the magnetisation also decreases below the critical value, which allows for Fermi acceleration to operate. Using this fact, we find a bulk Lorentz factor of $420 \simleq Γ\simleq 770$, and an emission radius of $R \sim 10^{18}$ cm, indicating a tenuous gas of the immediate circumburst surrounding. The observation of the onset of particle acceleration thus gives new and independent constraints on the properties of the flow as well as on theories of particle acceleration in collisionless astrophysical shocks.

astro-ph.HE↗