arXiv subjects

Akram Ibrahim

Publications and source records attributed to Akram Ibrahim.

5 recordsLinked to original sources

arXiv2025

Data-Driven Molecular Dynamics and TEM Analysis of Crystal Growth and Hydrogen Sensing in Pt-Functionalized Graphene Chemiresistive Sensors

Graphene functionalized with catalytic transition metals offers high-performance gas sensing by coupling graphene's exceptional electronic transport properties with the metal's catalytic activity, yet the atomistic relationships connecting synthesis parameters, morphological outcomes, and sensor performance remain elusive. We developed an equivariant machine-learning interatomic potential (MLIP) with near-DFT accuracy to perform molecular dynamics (MD) simulations of Pt crystal growth on graphene and subsequent H$_2$-sensing. MD simulations validated by TEM show that Pt deposition begins with dispersed nuclei coalescing into polycrystalline nanoclusters with predominantly FCC interiors, while both MD and Raman spectroscopy reveal a predominantly non-covalent Pt-graphene interaction that induces moderate local strain and charge transfer yet preserves graphene's structural integrity. Reactive MD simulations confirm H$_2$ dissociative chemisorption exclusively on Pt, with negligible spillover onto pristine graphene. However, H adsorption on Pt attenuates the Pt-graphene interfacial binding, providing an indirect electronic pathway for H$_2$ sensing. Transient and steady-state kinetic analyses demonstrate that an intermediate Pt loading minimizes the limit of detection; lower loadings facilitate faster response and recovery kinetics and enhance signal transduction, whereas higher loadings increase the doping level of graphene. DFT charge analysis indicates that under-coordinated Pt clusters induce $n$-type doping in graphene, whereas continuous Pt films induce $p$-type doping, with both effects attenuated upon H adsorption. The developed machine-learned MD framework enables quantum-mechanically accurate modeling of metal crystal growth on graphene, elucidates the underlying H$_2$-sensing mechanism, and correlates several key sensing figures of merit with metal loading and morphology.

cond-mat.mtrl-sci↗

arXiv2024

Quantum Monte Carlo and density functional theory study of strain and magnetism in 2D 1T-VSe$_2$ with charge density wave states

Two-dimensional (2D) 1T-VSe$_2$ has prompted significant interest due to the discrepancies regarding alleged ferromagnetism (FM) at room temperature, charge density wave (CDW) states and the interplay between the two. We employed a combined Diffusion Monte Carlo (DMC) and density functional theory (DFT) approach to accurately investigate the magnetic properties, CDW states, and their response to strain in monolayer 1T-VSe$_2$. Our calculations show the delicate competition between various phases, revealing critical insights into the relationship between their energetic and structural properties. We performed classical Monte Carlo simulations informed by our DMC and DFT results, and found the magnetic transition temperature ($T_c$) of the undistorted (non-CDW) FM phase to be 228 K and the distorted (CDW) phase to be 68 K. Additionally, we studied the response of biaxial strain on the energetic stability and magnetic properties of various phases of 2D 1T-VSe$_2$ and found that small amounts of strain can increase the $T_c$, suggesting a promising route for engineering and enhancing magnetic behavior. Finally, we synthesized 1T-VSe$_2$ and performed Raman spectroscopy measurements, which were in close agreement with our calculated results, validating our computational approach. Our work emphasizes the role of highly accurate DMC methods in advancing the understanding of monolayer 1T-VSe$_2$ and provides a robust framework for future studies of 2D magnetic materials.

cond-mat.mtrl-sci↗

arXiv2024

Prediction of Frequency-Dependent Optical Spectrum for Solid Materials: A Multi-Output & Multi-Fidelity Machine Learning Approach

The frequency-dependent optical spectrum is pivotal for a broad range of applications, from material characterization to optoelectronics and energy harvesting. Data-driven surrogate models, trained on density functional theory (DFT) data, have effectively alleviated the scalability limitations of DFT while preserving its chemical accuracy, expediting material discovery. However, prevailing machine learning (ML) efforts often focus on scalar properties such as the band gap, overlooking the complexities of optical spectra. In this work, we employ deep graph neural networks (GNNs) to predict the frequency-dependent complex-valued dielectric function across the infrared, visible, and ultraviolet spectra directly from crystal structures. We explore multiple architectures for multi-output spectral representation of the dielectric function and utilize various multi-fidelity learning strategies, such as transfer learning and fidelity embedding, to address the challenges associated with the scarcity of high-fidelity DFT data. Additionally, we model key solar cell absorption efficiency metrics, demonstrating that learning these parameters is enhanced when integrated through a learning bias within the learning of the frequency-dependent absorption coefficient. This study demonstrates that leveraging multi-output and multi-fidelity ML techniques enables accurate predictions of optical spectra from crystal structures, providing a versatile tool for rapidly screening materials for optoelectronics, optical sensing, and solar energy applications across an extensive frequency spectrum.

physics.chem-ph↗

arXiv2023

Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations

The chemical exfoliation of non-van der Waals (vdW) materials to ultrathin nanosheets remains a formidable challenge. This difficulty arises from the strong preference of these materials to engage in three-dimensional chemical bonding, resulting in uncontrolled atomic migration into the vdW gaps during cation deintercalation from the bulk structure, ultimately leading to unpredictable structural disorder. We propose a generic framework using neural network potentials (NNPs) to accurately model the widespread nonstoichiometric environments resulting from disordered atomic migrations during exfoliation of non-vdW materials. We apply our framework to investigate the crystal structures and phase transformations occurring during the exfoliation of non-vdW nonstoichiometric Cr$_{(1-x)}$S systems, a compelling material category with substantial potential for two-dimensional (2D) magnetic applications. The efficacy of the NNP outperforms the conventional cluster expansion, exhibiting superior accuracy and transferability to unexplored crystal structures and compositions. By employing the NNP in simulated annealing optimizations, we predict low-energy Cr$_{(1-x)}$S structures anticipated to result from experimental synthesis. A notable structural transition is discerned at the Cr$_{0.5}$S composition, with half of the Cr atoms preferentially migrating to vdW gaps. This aligns with experimental observations in the chemical exfoliation of 2D CrS$_2$, and emphasizes the vital role of excess Cr atoms beyond the Cr/S = $1/2$ composition ratio in stabilizing vdW gaps. Additionally, we utilize the NNP in a vacancy diffusion Monte Carlo simulation to illustrate the impact of lateral compressive strains in catalyzing the formation of vdW gaps within non-vdW CrS$_2$ slabs. This provides a direct pathway for more facile exfoliation of ultrathin nanosheets from non-vdW materials through strain engineering.

cond-mat.mtrl-sci↗

arXiv2021

Comprehensive Study of Lithium Adsorptionand Diffusion on Janus Mo/WXY (X, Y= S,Se, Te) using First Principles and MachineLearning Approaches

The structural asymmetry of two-dimensional (2D) Janus transition metal dichalcogenides (TMDs) produces internal dipole moments that result in interesting electronic properties. These properties differ from the regular (symmetric) TMD structures that the Janus structures are derived from. In this study, we, at first, examine adsorption and diffusion of a single Li atom on regular MX2and Janus MXY (M = Mo, W; XY =S, Se, Te) TMD structures at various concentrations using first principles calculations within density functional theory. To gain more physical insight and prepare for future investigations of regular TMD and Janus materials, we applied a supervised machine learning (ML) model that uses cluster-wise linear regression to predict the adsorption energies of Li on top of 2D TMDs. We developed a universal representation with few descriptors that take into account the intrinsic dipole moment and the electronic structure of regular and Janus 2D layers, the side where the adsorption takes place and the concentration dependence of adatom doping. This representation can easily be generalized to be used for other impurities and 2D layer combinations, including alloys as well. At last, we focus on analyzing these structures as possible anodes in battery applications. We conducted Li diffusion, open-circuit-voltage and storage capacity simulations. We report that Lithium atoms are found to easily migrate between transition metal (Mo, W) top sites for each considered case, and in these respects many of the examined Janus materials are comparable or superior to graphene and to regular TMDs. The results imply that theexamined Janus structures should perform well as electrodes in Li-ion batteries.

cond-mat.mtrl-sci↗