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Attila Cangi

Publications and source records attributed to Attila Cangi.

At least 37 records · Page 2Linked to original sources
arXiv2023

Ab initio insights on the ultrafast strong-field dynamics of anatase TiO$_2$

Electron dynamics of anatase TiO$_2$ under the influence of ultrashort and intense laser field is studied using the real-time time-dependent density functional theory (TDDFT). Our findings demonstrate the effectiveness of TDDFT calculations in modeling the electron dynamics of solids during ultrashort laser excitation, providing valuable insights for designing and optimizing nonlinear photonic devices. We analyze the perturbative and non-perturbative responses of TiO$_2$ to 30 fs laser pulses at 400 and 800 nm wavelengths, elucidating the underlying mechanisms. At 400 nm, ionization via single photon absorption dominates, even at very low intensities. At 800 nm, we observe ionization through two-photon absorption within the intensity range of $1\times10^{10}$ to $9\times10^{12}$ W/cm$^2$, with a transition from multiphoton to tunneling ionization occurring at $9\times10^{12}$ W/cm$^2$. We observe a sudden increase in energy and the number of excited electrons beyond $1\times10^{13}$ W/cm$^2$, leading to their saturation and subsequent laser-induced damage. We estimate the damage threshold of TiO$_2$ for 800 nm to be 0.1 J/cm$^2$. In the perturbative regime, induced currents exhibit a phase shift proportional to the peak intensity of the laser pulse. This phase shift is attributed to the intensity-dependent changes in the number of free carriers, indicative of the optical Kerr effect. Leveraging the linear dependence of phase shift on peak intensities, we estimate the nonlinear refractive index ($n_2$) of TiO$_2$ to be $3.54\times10^{-11}$ cm$^2$/W.

physics.optics↗
arXiv2023

Impact of Electronic Correlations on High-Pressure Iron: Insights from Time-Dependent Density Functional Theory

We present a comprehensive investigation of the electrical and thermal conductivity of iron under high pressures at ambient temperature, employing the real-time formulation of time-dependent density functional theory (RT-TDDFT). Specifically, we examine the influence of a Hubbard correction (+\textit{U}) to account for strong electron correlations. Our calculations based on RT-TDDFT demonstrate that the evaluated electrical conductivity for both high-pressure body-centered cubic (BCC) and hexagonal close-packed (HCP) iron phases agrees well with experimental data. Furthermore, we explore the anisotropy in the thermal conductivity of HCP iron under high pressure, and our findings are consistent with experimental observations. Interestingly, we find that the incorporation of the +\textit{U} correction significantly impacts the ground state and linear response properties of iron at pressures below 50 GPa, with its influence diminishing as pressure increases. This study offers valuable insights into the influence of electronic correlations on the electronic transport properties of iron under extreme conditions.

cond-mat.mtrl-sci↗
arXiv2023

Transferable Interatomic Potentials for Aluminum from Ambient Conditions to Warm Dense Matter

We present a study on the transport and materials properties of aluminum spanning from ambient to warm dense matter conditions using a machine-learned interatomic potential (ML-IAP). Prior research has utilized ML-IAPs to simulate phenomena in warm dense matter, but these potentials have often been calibrated for a narrow range of temperature and pressures. In contrast, we train a single ML-IAP over a wide range of temperatures, using density functional theory molecular dynamics (DFT-MD) data. Our approach overcomes computational limitations of DFT-MD simulations, enabling us to study transport and materials properties of matter at higher temperatures and longer time scales. We demonstrate the ML-IAP transferability across a wide range of temperatures using molecular-dynamics (MD) by examining the thermal conductivity, diffusion coefficient, viscosity, sound velocity, and ion-ion structure factor of aluminum up to about 60,000 K, where we find good agreement with previous theoretical data.

cond-mat.mtrl-sci↗
arXiv2023

Improved calculations of mean ionization states with an average-atom model

The mean ionization state (MIS) is a critical property in dense plasma and warm dense matter research, for example as an input to hydrodynamics simulations and Monte-Carlo simulations. Unfortunately, however, the best way to compute the MIS remains an open question. Average-atom (AA) models are widely-used in this context due to their computational efficiency, but as we show here, the canonical approach for calculating the MIS in AA models is typically insufficient. We therefore explore three alternative approaches to compute the MIS. Firstly, we modify the canonical approach to change the way electrons are partitioned into bound and free states; secondly, we develop a novel approach using the electron localization function; finally, we extend a method which uses the Kubo-Greenwood conductivity to our average-atom model. Through comparisons with higher-fidelity simulations and experimental data, we find that any of the three new methods usually out-performs the canonical approach, with the electron localization function and Kubo-Greenwood methods showing particular promise.

physics.plasm-ph↗
arXiv2023

Improving dynamic collision frequencies: impacts on dynamic structure factors and stopping powers in warm dense matter

Simulations and diagnostics of high-energy-density plasmas and warm dense matter rely on models of material response properties, both static and dynamic (frequency-dependent). Here, we systematically investigate variations in dynamic electron-ion collision frequencies $ν(ω)$ in warm dense matter using data from a self-consistent-field average-atom model. We show that including the full quantum density of states, strong collisions, and inelastic collisions lead to significant changes in $ν(ω)$. These changes result in red shifts and broadening of the plasmon peak in the dynamic structure factor, an effect observable in x-ray Thomson scattering spectra, and modify stopping powers around the Bragg peak. These changes improve the agreement of computationally efficient average-atom models with first-principles time-dependent density functional theory in warm dense aluminum, carbon, and deuterium.

physics.plasm-ph↗
arXiv2022

Temperature analysis of X-ray Thomson scattering data

The accurate interpretation of experiments with matter at extreme densities and pressures is a notoriously difficult challenge. In a recent work [T.~Dornheim et al., Nature Comm. (in print), arXiv:2206.12805], we have introduced a formally exact methodology that allows extracting the temperature of arbitrarily complex materials without any model assumptions or simulations. Here, we provide a more detailed introduction to this approach and analyze the impact of experimental noise on the extracted temperatures. In particular, we extensively apply our method both to synthetic scattering data and to previous experimental measurements over a broad range of temperatures and wave numbers. We expect that our approach will be of high interest to a gamut of applications, including inertial confinement fusion, laboratory astrophysics, and the compilation of highly accurate equation-of-state databases.

physics.plasm-ph↗
arXiv2022

Electronic Density Response of Warm Dense Matter

Matter at extreme temperatures and pressures -- commonly known as warm dense matter (WDM) in the literature -- is ubiquitous throughout our Universe and occurs in a number of astrophysical objects such as giant planet interiors and brown dwarfs. Moreover, WDM is very important for technological applications such as inertial confinement fusion, and is realized in the laboratory using different techniques. A particularly important property for the understanding of WDM is given by its electronic density response to an external perturbation. Such response properties are routinely probed in x-ray Thomson scattering (XRTS) experiments, and, in addition, are central for the theoretical description of WDM. In this work, we give an overview of a number of recent developments in this field. To this end, we summarize the relevant theoretical background, covering the regime of linear-response theory as well as nonlinear effects, the fully dynamic response and its static, time-independent limit, and the connection between density response properties and imaginary-time correlation functions (ITCF). In addition, we introduce the most important numerical simulation techniques including ab initio path integral Monte Carlo (PIMC) simulations and different thermal density functional theory (DFT) approaches. From a practical perspective, we present a variety of simulation results for different density response properties, covering the archetypal model of the uniform electron gas and realistic WDM systems such as hydrogen. Moreover, we show how the concept of ITCFs can be used to infer the temperature from XRTS measurements of arbitrarily complex systems without the need for any models or approximations. Finally, we outline a strategy for future developments based on the close interplay between simulations and experiments.

physics.plasm-ph↗
arXiv2022

Predicting electronic structures at any length scale with machine learning

The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful to the point of being recognized with a Nobel prize in 1998, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances science to frontiers intractable with any current solutions. This unprecedented modeling capability opens up an inexhaustible range of applications in astrophysics, novel materials discovery, and energy solutions for a sustainable future.

cond-mat.mtrl-sci↗
arXiv2022

Non-empirical mixing coefficient for hybrid XC functionals from analysis of the XC kernel

We present an analysis of the static exchange-correlation (XC) kernel computed from hybrid functionals with a single mixing coefficient such as PBE0 and PBE0-1/3. We break down the hybrid XC kernels into the exchange and correlation parts using the Hartree-Fock functional, the exchange-only PBE, and the correlation-only PBE. This decomposition is combined with exact data for the static XC kernel of the uniform electron gas and an Airy gas model within a subsystem functional approach. This gives us a tool for the nonempirical choice of the mixing coefficient at ambient and extreme conditions. Our analysis provides physical insights into the effect of the variation of the mixing coefficient in hybrid functionals, which is of immense practical value. The presented approach is general and can be used for other type of functionals like screened hybrids.

physics.chem-ph↗
arXiv2022

Assessing the accuracy of hybrid exchange-correlation functionals for the density response of warm dense electrons

We assess the accuracy of common hybrid exchange-correlation (XC) functionals (PBE0, PBE0-1/3, HSE06, HSE03, and B3LYP) within Kohn-Sham density functional theory (KS-DFT) for the harmonically perturbed electron gas at parameters relevant for the challenging conditions of warm dense matter. Generated by laser-induced compression and heating in the laboratory, warm dense matter is a state of matter that also occurs in white dwarfs and planetary interiors. We consider both weak and strong degrees of density inhomogeneity induced by the external field at various wavenumbers. We perform an error analysis by comparing to exact quantum Monte-Carlo results. In the case of a weak perturbation, we report the static linear density response function and the static XC kernel at a metallic density for both the degenerate ground-state limit and for partial degeneracy at the electronic Fermi temperature. Overall, we observe an improvement in the density response for partial degeneracy when the PBE0, PBE0-1/3, HSE06, and HSE03 functionals are used compared to the previously reported results for the PBE, PBEsol, LDA, AM05, and SCAN functionals; B3LYP, on the other hand, does not perform well for the considered system. Together with the reduction of self-interaction errors, this seems to be the rationale behind the relative success of the HSE03 functional for the description of the experimental data on aluminum and liquid ammonia at WDM conditions.

physics.chem-ph↗
arXiv2022

Accelerating Equilibration in First-Principles Molecular Dynamics with Orbital-Free Density Functional Theory

We introduce a practical hybrid approach that combines orbital-free density functional theory (DFT) with Kohn-Sham DFT for speeding up first-principles molecular dynamics simulations. Equilibrated ionic configurations are generated using orbital-free DFT for subsequent Kohn-Sham DFT molecular dynamics. This leads to a massive reduction of the simulation time without any sacrifice in accuracy. We assess this finding across systems of different sizes and temperature, up to the warm dense matter regime. To that end, we use the cosine distance between the time series of radial distribution functions representing the ionic configurations. Likewise, we show that the equilibrated ionic configurations from this hybrid approach significantly enhance the accuracy of machine-learning models that replace Kohn-Sham DFT. Our hybrid scheme enables systematic first-principles simulations of warm dense matter that are otherwise hampered by the large numbers of atoms and the prevalent high temperatures. Moreover, our finding provides an additional motivation for developing kinetic and noninteracting free energy functionals for orbital-free DFT.

cond-mat.mtrl-sci↗
arXiv2022

atoMEC: An open-source average-atom Python code

Average-atom models are an important tool in studying matter under extreme conditions, such as those conditions experienced in planetary cores, brown and white dwarfs, and during inertial confinement fusion. In the right context, average-atom models can yield results with similar accuracy to simulations which require orders of magnitude more computing time, and thus can greatly reduce financial and environmental costs. Unfortunately, due to the wide range of possible models and approximations, and the lack of open-source codes, average-atom models can at times appear inaccessible. In this paper, we present our open-source average-atom code, atoMEC. We explain the aims and structure of atoMEC to illuminate the different stages and options in an average-atom calculation, and to facilitate community contributions. We also discuss the use of various open-source Python packages in atoMEC, which have expedited its development.

physics.plasm-ph↗
arXiv2022

Training-free hyperparameter optimization of neural networks for electronic structures in matter

A myriad of phenomena in materials science and chemistry rely on quantum-level simulations of the electronic structure in matter. While moving to larger length and time scales has been a pressing issue for decades, such large-scale electronic structure calculations are still challenging despite modern software approaches and advances in high-performance computing. The silver lining in this regard is the use of machine learning to accelerate electronic structure calculations -- this line of research has recently gained growing attention. The grand challenge therein is finding a suitable machine-learning model during a process called hyperparameter optimization. This, however, causes a massive computational overhead in addition to that of data generation. We accelerate the construction of neural network models by roughly two orders of magnitude by circumventing excessive training during the hyperparameter optimization phase. We demonstrate our workflow for Kohn-Sham density functional theory, the most popular computational method in materials science and chemistry.

cond-mat.mtrl-sci↗
arXiv2022

An \textit{ab initio} study of shock-compressed copper

We investigate shock-compressed copper in the warm dense matter regime by means of density functional theory molecular dynamics simulations. We use neural-network-driven interatomic potentials to increase the size of the simulation box and extract thermodynamic properties in the hydrodynamic limit. We show the agreement of our simulation results with experimental data for solid copper at ambient conditions and liquid copper near the melting point under ambient pressure. Furthermore, a thorough analysis of the dynamic ion-ion structure factor in shock-compressed copper is performed and the adiabatic speed of sound is extracted and compared with experimental data.

cond-mat.other↗
arXiv2022

Effective electronic forces and potentials from ab initio path integral Monte Carlo simulations

The rigorous description of correlated quantum many-body systems constitutes one of the most challenging tasks in contemporary physics and related disciplines. In this context, a particularly useful tool is the concept of effective pair potentials that take into account the effects of the complex many-body medium consistently. In this work, we present extensive, highly accurate \emph{ab initio} path integral Monte Carlo (PIMC) results for the effective interaction and the effective force between two electrons in the presence of the uniform electron gas (UEG). This gives us a direct insight into finite-size effects, thereby opening up the possibility for novel domain decompositions and methodological advances. In addition, we present unassailable numerical proof for an effective attraction between two electrons under moderate coupling conditions, without the mediation of an underlying ionic structure. Finally, we compare our exact PIMC results to effective potentials from linear-response theory, and we demonstrate their usefulness for the description of the dynamic structure factor. All PIMC results are made freely available online and can be used as a thorough benchmark for new developments and approximations.

physics.comp-ph↗
arXiv2022

A Scalable 5,6-Qubit Grover's Quantum Search Algorithm

Recent studies have been spurred on by the promise of advanced quantum computing technology, which has led to the development of quantum computer simulations on classical hardware. Grover's quantum search algorithm is one of the well-known applications of quantum computing, enabling quantum computers to perform a database search (unsorted array) and quadratically outperform their classical counterparts in terms of time. Given the restricted access to database search for an oracle model (black-box), researchers have demonstrated various implementations of Grover's circuit for two to four qubits on various platforms. However, larger search spaces have not yet been explored. In this paper, a scalable Quantum Grover Search algorithm is introduced and implemented using 5-qubit and 6-qubit quantum circuits, along with a design pattern for ease of building an Oracle for a higher order of qubits. For our implementation, the probability of finding the correct entity is in the high nineties. The accuracy of the proposed 5-qubit and 6-qubit circuits is benchmarked against the state-of-the-art implementations for 3-qubit and 4-qubit. Furthermore, the reusability of the proposed quantum circuits using subroutines is also illustrated by the opportunity for large-scale implementation of quantum algorithms in the future.

quant-ph↗
arXiv2022

Electrical Conductivity of Iron in Earth's Core from Microscopic Ohm's Law

Understanding the electronic transport properties of iron under high temperatures and pressures is essential for constraining geophysical processes. The difficulty of reliably measuring these properties under Earth-core conditions calls for sophisticated theoretical methods that can support diagnostics. We present results of the electrical conductivity within the pressure and temperature ranges found in Earth's core from simulating microscopic Ohm's law using time-dependent density functional theory. Our predictions provide a new perspective on resolving discrepancies in recent experiments.

cond-mat.mtrl-sci↗