arXiv subjects

Adela Habib

Publications and source records attributed to Adela Habib.

11 recordsLinked to original sources

arXiv2026

An Agentic Orchestration of Atomistic Simulations

Atomistic simulations are central to materials design, but their execution involves complex, multi-step workflows that require significant human expertise. Here, we present an agent-based system embedded within the URSA (Universal Research and Scientific Agent) framework that automates the design, execution, and validation of atomistic simulations, demonstrated using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) tool. Our system autonomously selects interatomic potentials, constructs and runs simulations, and performs iterative error recovery within a closed-loop workflow. We evaluate the scientific reliability of the agent by benchmarking its outputs against LAVA, a high-throughput toolkit for LAMMPS and the Vienna Ab initio Simulation Package (VASP) calculations. Our framework reduces manual intervention and trial-and-error, thereby improving the rigor, reproducibility, and scalability of atomistic modeling.

cs.AI↗

arXiv2024

Magnetic-field dependence of spin-phonon relaxation and dephasing due to g-factor fluctuations from first principles

The electron spin decay lifetime in materials can be characterized by relaxation (T1) and irreversible (T2) and reversible (T2*) decoherence processes. Their interplay leads to a complex dependence of spin relaxation times on the direction and magnitude of magnetic fields, relevant for spintronics and quantum information applications. Here, we use real-time first-principles density matrix dynamics simulations to directly simulate Hahn echo measurements, disentangle dephasing from decoherence, and predict T1, T2 and T2* spin lifetimes. We show that g-factor fluctuations lead to non-trivial magnetic field dependence of each of these lifetimes in inversion-symmetric crystals of CsPbBr3 and silicon, even when only intrinsic spin-phonon scattering is present. Most importantly, fluctuations in the off-diagonal components of the g-tensor lead to a strong magnetic field dependence of even the T1 lifetime in silicon. Our calculations elucidate the detailed role of anisotropic g-factors in determining the spin dynamics even in simple, low spin-orbit coupling materials such as silicon.

cond-mat.mtrl-sci↗

arXiv2024

Susceptibility Formulation of Density Matrix Perturbation Theory

Density matrix perturbation theory based on recursive Fermi-operator expansions provides a computationally efficient framework for time-independent response calculations in quantum chemistry and materials science. From a perturbation in the Hamiltonian we can calculate the first-order perturbation in the density matrix, which then gives us the linear response in the expectation values for some chosen set of observables. Here we present an alternative, {\it dual} formulation, where we instead calculate the static susceptibility of an observable, which then gives us the linear response in the expectation values for any number of different Hamiltonian perturbations. We show how the calculation of the susceptibility can be performed with the same expansion schemes used in recursive density matrix perturbation theory, including generalizations to fractional occupation numbers and self-consistent linear response calculations, i.e. similar to density functional perturbation theory. As with recursive density matrix perturbation theory, the dual susceptibility formulation is well suited for numerically thresholded sparse matrix algebra, which has linear scaling complexity for sufficiently large sparse systems. Similarly, the recursive computation of the susceptibility also seamlessly integrates with the computational framework of deep neural networks used in artificial intelligence (AI) applications. This integration enables the calculation of quantum response properties that can leverage cutting-edge AI-hardware, such as Nvidia Tensor cores or Google Tensor Processing Units. We demonstrate performance for recursive susceptibility calculations using Nvidia Graphics Processing Units and Tensor cores.

quant-ph↗

arXiv2024

Efficient Mixed-Precision Matrix Factorization of the Inverse Overlap Matrix in Electronic Structure Calculations with AI-Hardware and GPUs

In recent years, a new kind of accelerated hardware has gained popularity in the Artificial Intelligence (AI) and Machine Learning (ML) communities which enables extremely high-performance tensor contractions in reduced precision for deep neural network calculations. In this article, we exploit Nvidia Tensor cores, a prototypical example of such AI/ML hardware, to develop a mixed precision approach for computing a dense matrix factorization of the inverse overlap matrix in electronic structure theory, $S^{-1}$. This factorization of $S^{-1}$, written as $ZZ^T=S^{-1}$, is used to transform the general matrix eigenvalue problem into a standard matrix eigenvalue problem. Here we present a mixed precision iterative refinement algorithm where $Z$ is given recursively using matrix-matrix multiplications and can be computed with high performance on Tensor cores. To understand the performance and accuracy of Tensor cores, comparisons are made to GPU-only implementations in single and double precision. Additionally, we propose a non-parametric stopping criteria which is robust in the face of lower precision floating point operations. The algorithm is particularly useful when we have a good initial guess to $Z$, for example, from previous time steps in quantum-mechanical molecular dynamics simulations or from a previous iteration in a geometry optimization.

physics.comp-ph↗

arXiv2023

Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles

Highly energetic electron-hole pairs (hot carriers) formed from plasmon decay in metallic nanostructures promise sustainable pathways for energy-harvesting devices. However, efficient collection before thermalization remains an obstacle for realization of their full energy generating potential. Addressing this challenge requires detailed understanding of physical processes from plasmon excitation in metal to their collection in a molecule or a semiconductor, where atomistic theoretical investigation may be particularly beneficial. Unfortunately, first-principles theoretical modeling of these processes is extremely costly, limiting the analysis to systems with a few 100s of atoms. Recent advances in machine learned interatomic potentials suggest that dynamics can be accelerated with surrogate models which replace the full solution of the Schroedinger Equation. Here, we modify an existing neural network, Hierarchically Interacting Particle Neural Network (HIP-NN), to predict plasmon dynamics in Ag nanoparticles. We demonstrate the model's capability to accurately predict plasmon dynamics in large nanoparticles of up to 561 atoms not present in the training dataset. More importantly, with machine learning models we gain a speed-up of about 200 times as compared with the rt-TDDFT calculations when predicting important physical quantities such as dynamic dipole moments in Ag55 and about 4000 times for extended nanoparticles that are 10 times larger. This underscores the promise of future machine learning accelerated electron/nuclear dynamics simulations for understanding fundamental properties of plasmon-driven hot carrier devices.

cond-mat.mes-hall↗

arXiv2022

Electric fields and substrates dramatically accelerate spin relaxation in graphene

Electrons in graphene are theoretically expected to retain spin states much longer than most materials, making graphene a promising platform for spintronics and quantum information technologies. Here, we use first-principles density-matrix (FPDM) dynamics simulations to show that interaction with electric fields and substrates strongly enhance spin relaxation through scattering with phonons. Consequently, the relaxation time at room temperature reduces from microseconds in free-standing graphene to nanoseconds in graphene on hexagonal boron nitride (hBN) substrate, the order of magnitude typically measured in experiments. Further, inversion symmetry breaking by hBN introduces a stronger asymmetry in electron and hole spin lifetimes, than predicted by the conventional D'yakonov-Perel' (DP) model for spin relaxation. Deviations from the conventional DP model are stronger for in-plane spin relaxation, resulting in out-of-plane to in-plane lifetime ratios much greater than 1/2 with a maximum close to the Dirac point. These FPDM results, independent of symmetry-specific assumptions or material-dependent parameters, also validate recent modifications of the DP model to explain such deviations. Overall, our results indicate that spin-phonon relaxation in the presence of substrates may be more important in graphene than typically assumed, requiring consideration for graphene-based spin technologies at room temperature.

cond-mat.mtrl-sci↗

arXiv2021

Giant Spin Lifetime Anisotropy and Spin-Valley Locking in Silicene and Germanene from First-Principles Density-Matrix Dynamics

Through First-Principles real-time Density-Matrix (FPDM) dynamics simulations, we investigate spin relaxation due to electron-phonon and electron-impurity scatterings with spin-orbit coupling in two-dimensional Dirac materials - silicene and germanene, at finite temperatures and under external fields. We discussed the applicability of conventional descriptions of spin relaxation mechanisms by Elliott-Yafet (EY) and D'yakonov-Perel' (DP) compared to our FPDM method, which is determined by a complex interplay of intrinsic spin-orbit coupling, external fields, and electron-phonon coupling strength, beyond crystal symmetry. For example, the electric field dependence of spin relaxation time is close to DP mechanism for silicene at room temperature, but rather similar to EY mechanism for germanene. Due to its stronger spin-orbit coupling strength and buckled structure in sharp contrast to graphene, germanene has a giant spin lifetime anisotropy and spin valley locking effect under nonzero Ez and relatively low temperature. More importantly, germanene has extremely long spin lifetime (~100 ns at 50 K) and ultrahigh carrier mobility, which makes it advantageous for spin-valleytronic applications.

cond-mat.mtrl-sci↗

arXiv2021

Ab initio Ultrafast Spin Dynamics in Solids

Spin relaxation and decoherence is at the heart of spintronics and spin-based quantum information science. Currently, theoretical approaches that can accurately predict spin relaxation of general solids including necessary scattering pathways and capable for ns to ms simulation time are urgently needed. We present a first-principles real-time density-matrix approach based on Lindblad dynamics to simulate ultrafast spin dynamics for general solid-state systems. Through the complete first-principles descriptions of pump, probe and scattering processes including electron-phonon, electron-impurity and electron-electron scatterings with self-consistent electronic spin-orbit couplings, our method can directly simulate the ultrafast pump-probe measurements for coupled spin and electron dynamics over ns at any temperatures and doping levels. We first apply this method to a prototypical system GaAs and obtain excellent agreement with experiments. We find that the relative contributions of different scattering mechanisms and phonon modes differ considerably between spin and carrier relaxation processes. In sharp contrast to previous work based on model Hamiltonians, we point out that the electron-electron scattering is negligible at room temperature but becomes dominant at low temperatures for spin relaxation in n-type GaAs. We further examine ultrafast dynamics in novel spin-valleytronic materials - monolayer and bilayer WSe2 with realistic defects. We find that spin relaxation is highly sensitive to local symmetry and chemical bonds around defects. Our work provides a predictive computational platform for spin dynamics in solids, which has unprecedented potentials for designing new materials ideal for spintronics and quantum information technology.

cond-mat.mtrl-sci↗

arXiv2020

Spin-phonon relaxation from a universal \emph{ab initio} density-matrix approach

Designing new quantum materials with long-lived electron spin states urgently requires a general theoretical formalism and computational technique to reliably predict intrinsic spin relaxation times. We present a new, accurate and universal first-principles methodology based on Lindbladian dynamics of density matrices to calculate spin-phonon relaxation time ($τ_s$) of solids with arbitrary spin mixing and crystal symmetry. This method describes contributions of Elliott-Yafet (EY) and D'yakonov-Perel' (DP) mechanisms to spin relaxation for systems with and without inversion symmetry on an equal footing. We show that intrinsic spin and momentum relaxation times both decrease with increasing temperature; however, for the DP mechanism, spin relaxation time varies inversely with extrinsic scattering time. We predict large anisotropy of spin lifetime in transition metal dichalcogenides. The excellent agreement with experiments for a broad range of materials underscores the predictive capability of our method for properties critical to quantum information science.

cond-mat.mtrl-sci↗

arXiv2018

Ultrafast Studies of Hot-Hole Dynamics in Au/p-GaN Heterostructures

Harvesting non-equilibrium hot carriers from photo-excited metal nanoparticles has enabled plasmon-driven photochemical transformations and tunable photodetection with resonant nanoantennas. Despite numerous studies on the ultrafast dynamics of hot electrons, to date, the temporal evolution of hot holes in metal-semiconductor heterostructures remains unknown. An improved understanding of the carrier dynamics in hot-hole-driven systems is needed to help expand the scope of hot-carrier optoelectronics beyond hot-electron-based devices. Here, using ultrafast transient absorption spectroscopy, we show that plasmon-induced hot-hole injection from gold (Au) nanoparticles into the valence band of p-type gallium nitride (p-GaN) occurs within 200 fs, placing hot-hole transfer on a similar timescale as hot-electron transfer. We further observed that the removal of hot holes from below the Au Fermi level exerts a discernible influence on the thermalization of hot electrons above it, reducing the peak electronic temperature and decreasing the electron-phonon coupling time relative to Au samples without a pathway for hot-hole collection. First principles calculations corroborate these experimental observations, suggesting that hot-hole injection modifies the relaxation dynamics of hot electrons in Au nanoparticles through ultrafast modulation of the d-band electronic structure. Taken together, these ultrafast studies substantially advance our understanding of the temporal evolution of hot holes in metal-semiconductor heterostructures and suggest new strategies for manipulating and controlling the energy distributions of hot carriers on ultrafast timescales.

cond-mat.mes-hall↗

arXiv2018

Hot carrier dynamics in plasmonic transition metal nitrides

Extraction of non-equilibrium hot carriers generated by plasmon decay in metallic nanostructures is an increasingly exciting prospect for utilizing plasmonic losses, but the search for optimum plasmonic materials with long-lived carriers is ongoing. Transition metal nitrides are an exciting class of new plasmonic materials with superior thermal and mechanical properties compared to conventional noble metals, but their suitability for plasmonic hot carrier applications remains unknown. Here, we present fully first-principles calculations of the plasmonic response, hot carrier generation and subsequent thermalization of all group IV, V and VI transition metal nitrides, fully accounting for direct and phonon-assisted transitions as well as electron-electron and electron-phonon scattering. We find the largest frequency ranges for plasmonic response in ZrN, HfN and WN, between those of gold and silver, while we predict strongest absorption in the visible spectrum for the VN, NbN and TaN. Hot carrier generation is dominated by direct transitions for most of the relevant energy range in all these nitrides, while phonon-assisted processes dominate only below 1 eV plasmon energies primarily for the group IV nitrides. Finally, we predict the maximum hot carrier lifetimes to be around 10 fs for group IV and VI nitrides, a factor of 3 - 4 smaller than noble metals, due to strong electron-phonon scattering. However, we find longer carrier lifetimes for group V nitrides, comparable to silver for NbN and TaN, while exceeding 100 fs (twice that of silver) for VN, making them promising candidates for efficient hot carrier extraction.

cond-mat.mtrl-sci↗