Thousands Of Atoms Research Articles

A multi-reflection time-of-flight setup for the study of atomic clusters produced by magnetron sputtering

The Greifswald multi-reflection time-of-flight setup has been extended with a magnetron sputtering gas aggregation source for the production of atomic cluster ions with sizes ranging from a single to thousands of atoms. This source, combined with a newly added quadrupole mass filter and a linear Paul trap, opens up the possibility of many new atomic-cluster studies not feasible with the setup before. The new components and their interfacing with the previous setup are described, and benchmarking as well as the first experimental results are presented. The capability of the system to handle singly charged ions with masses of several ten thousand atomic mass units is demonstrated.

Second quantization of many-body dispersion interactions for chemical and biological systems

The many-body dispersion (MBD) framework is a successful approach for modeling the long-range electronic correlation energy and optical response of systems with thousands of atoms. Inspired by field theory, here we develop a second-quantized MBD formalism (SQ-MBD) that recasts a system of atomic quantum Drude oscillators in a Fock-space representation. SQ-MBD provides: (i) tools for projecting observables (interaction energy, transition multipoles, polarizability tensors) on coarse-grained representations of the atomistic system ranging from single atoms to large structural motifs, (ii) a quantum-information framework to analyze correlations and (non)separability among fragments in a given molecular complex, and (iii) a path toward the applicability of the MBD framework to molecular complexes with even larger number of atoms. The SQ-MBD approach offers conceptual insights into quantum fluctuations in molecular systems and enables direct coupling of collective plasmon-like MBD degrees of freedom with arbitrary environments, providing a tractable computational framework to treat dispersion interactions and polarization response in intricate systems.

Excited State Properties of Point Defects in Semiconductors and Insulators Investigated with Time-Dependent Density Functional Theory.

We present a formulation of spin-conserving and spin-flip hybrid time-dependent density functional theory (TDDFT), including the calculation of analytical forces, which allows for efficient calculations of excited state properties of solid-state systems with hundreds to thousands of atoms. We discuss an implementation on both GPU- and CPU-based architectures along with several acceleration techniques. We then apply our formulation to the study of several point defects in semiconductors and insulators, specifically the negatively charged nitrogen-vacancy and neutral silicon-vacancy centers in diamond, the neutral divacancy center in 4H silicon carbide, and the neutral oxygen-vacancy center in magnesium oxide. Our results highlight the importance of taking into account structural relaxations in excited states in order to interpret and predict optical absorption and emission mechanisms in spin defects.

Orchestrating the impact of antisites and vacancy defects on the elastic and optoelectronic properties of boron arsenide.

Cubic boron arsenide (c-BAs), a semiconducting material with ultra-high thermal conductivity and carrier mobilities, has been studied using first-principles calculation. This study examined the elastic and optoelectronic properties of c-BAs. The challenge of subphase boron (B) formation in bulk form owing to the volatile nature of arsenic (As) makes it mandatory to calculate its optoelectronic properties, by producing vacancies and antisite defects with BAs (As atom on a B site) and AsB (B atom on an As site). The mechanical properties including bulk (B), shear (G) moduli, and Poison's ratio of all the systems were studied. It was found that mechanical instability of the structure is observed for the overall vacancy creation, arsenic substitution, and mutual antisite defects. Further, pristine c-BAs showed an indirect bandgap of 1.48eV. Defect formation reduces the bandgap and shifts the absorption peaks, which improves the overall optoelectronic properties of the host material. In addition, B vacancy formation shows the maximum optical absorption and reflectivity and low energy loss, suggesting its potential applications for optoelectronic devices. The obtained anticipated data from this study is for the optoelectronic and elastic properties of c-BAs, for the device applications in photonics and electronics. In this paper, the elastic and optoelectronic properties of the pristine and defected c-BAs were systematically investigated using the Spanish Initiative for Electronic Simulations with Thousands of Atoms (SIESTA). The SIESTA program uses pseudopotentials in the norm-conserving nonlocal forms and pseudo-atomic orbital (PAO) basis set with a double-zeta potential (DZP) which are fundamental for calculating the Hamiltonian and overlap matrices in O(N) operations.

Efficient Molecular Dynamics Simulations of Deep Eutectic Solvents with First-Principles Accuracy Using Machine Learning Interatomic Potentials.

In recent years, deep eutectic solvents emerged as highly tunable and ecofriendly alternatives to common organic solvents and liquid electrolytes. In the present work, the ability of machine learning (ML) interatomic potentials for molecular dynamics (MD) simulations of these liquids is explored, showcasing a trained neural network potential for a 1:2 ratio mixture of choline chloride and urea (reline). Using the ML potentials trained on density functional theory data, MD simulations for large systems of thousands of atoms and nanosecond-long time scales are feasible at a fraction of the computational cost of the target first-principles simulations. The obtained structural and dynamical properties of reline from MD simulations using our machine learning models are in good agreement with the first-principles MD simulations and experimental results. Running a single MD simulation is highlighted as a general shortcoming of typical first-principles studies if the dynamic properties are investigated. Furthermore, velocity cross-correlation functions are employed to study the collective dynamics of the molecular components in reline.

Development of cat-GRRM/MC/MD method for the simulation of cross-linked network structure formation with molecular autocatalysis

Epoxy resins, which are common matrices in carbon fibre-reinforced plastics, are derived from the curing of epoxy amines. Molecular dynamics (MD) simulations are commonly used to model the physical properties of such network polymers, but they do not account for chemical reactions. Quantum chemical (QC) calculations can elucidate reaction mechanisms and energies but are not suitable for models with thousands of atoms. To address these limitations, in this study, we developed a new method, cat-GRRM/MC/MD, that combines QC calculations in the Global Reaction Route Mapping (GRRM) program with reaction simulations involved in MD simulation methods. This approach allows the simulation of reaction pathways in which proton donor molecules such as alcohols and amines act as molecular catalysts (autocatalysis), and the reaction kinetics are realised in a Monte Carlo (MC) manner. Our results show that autocatalysis significantly reduces the activation energy of the epoxy resin reaction, bringing the calculated data closer to the experimental results. When these quantum chemical calculations were incorporated into MD simulations, it was found that the activation energy influenced the crosslinking results. Models including catalysis led to network polymers with larger carbon skeletons, and physical properties such as the Young's modulus and glass transition temperature become closer to experimental results. This study not only deepens our understanding of the mechanism of the self-catalytic reaction of epoxy resins but also provides a new tool for accurately simulating the performance of epoxy resins, thus enabling more precise control of their properties.

Impact of Static Distortion Waves on Superlubricity.

Friction is a major source of energy loss in mechanical devices. This energy loss may be minimized by creating interfaces with extremely reduced friction, i.e., superlubricity. Conventional wisdom holds that incommensurate interface structures facilitate superlubricity. Accurately describing friction necessitates the precise modeling of the interface structure. This, in turn, requires the use of accurate first-principles electronic structure methods, especially when studying organic/metal interfaces, which are highly relevant due to their tunability and propensity to form incommensurate structures. However, the system size required to calculate incommensurate structures renders such calculations intractable. As a result, studies of incommensurate interfaces have been limited to very simple model systems or strongly simplified methodology. We overcome this limitation by developing a machine-learned interatomic potential that is able to determine energies and forces for structures containing thousands to tens of thousands of atoms with an accuracy comparable to conventional first-principles methods but at a fraction of the cost. Using this approach, we quantify the breakdown of superlubricity in incommensurate structures due to the formation of static distortion waves. Moreover, we extract design principles to engineer incommensurate interface systems where the formation of static distortion waves is suppressed, which facilitates low friction coefficients.

Growth Anisotropy and Morphology Evolution of Line Defects in Monolayer MoS2 : Atomic-Level Observation, Large-Scale Statistics, and Mechanism Understanding.

Understanding the growth behavior and morphology evolution of defects in 2D transition metal dichalcogenides is significant for the performance tuning of nanoelectronic devices. Here, the low-voltage aberration-corrected transmission electron microscopy with an in situ heating holder and a fast frame rate camera to investigate the sulfur vacancy lines in monolayer MoS2 is applied. Vacancy concentration-dependent growth anisotropy is discovered, displaying first lengthening and then broadening of line defects as the vacancy densifies. With the temperature increase from 20 °C to 800 °C, the defect morphology evolves from a dense triangular network to an ultralong linear structure due to the temperature-sensitive vacancy migration process. Atomistic dynamics of line defect reconstruction on the millisecond time scale are also captured. Density functional theory calculations, Monte Carlo simulation, and configurational force analysis are implemented to understand the growth and reconstruction mechanisms at relevant time and length scales. Throughout the work, high-resolution imaging is closely combined with quantitative analysis of images involving thousands of atoms so that the atomic-level structure and the large-area statistical rules are obtained simultaneously. The work provides new ideas for balancing the accuracy and universality of discoveries in the TEM study and will be helpful to the controlled sculpture of nanomaterials.

Phenomenological model for Raman spectra calculating based on “bond charge” taking into account deformation and electro‐optical mechanisms, including Fröhlich scattering

AbstractAn approach is proposed that combines the deformation and electro‐optical mechanisms of Raman scattering, including Fröhlich scattering. Eigenwavenumbers and eigenvectors of phonon modes with a given wave vector are calculated in the “bond charge” model. The Raman spectra were calculated using the Wolkenstein additive polarizability model. The proposed approach has been tested for modeling the Raman spectra of short‐period GaAs/AlAs superlattices containing tens of atoms in a primitive cell, but can also be used to calculate the Raman spectra of various nanoobjects containing thousands of atoms in a primitive cell.

Atomistic Simulation of Nanoscale Devices

Device simulation is nowa­days fully integrated into the production tool chain of transistors. The geometry of the latter can be carefully optimized, possible design pitfalls can be identified early on, and the obtained experimental data can be analyzed in detail thanks to state-of-the-art technology computer aided design tools. However, on the one hand, the dimensions of transistors are reaching the atomic scale. On the other hand, novel functionalities (e.g., light emission/detection) and materials, for example III-V semiconductors, are being added to silicon-based chips. To cope with these challenges it is crucial that device simulators go beyond classical theories, pure electronic transport, and continuum models. The inclusion of quantum mechanical phenomena, electro-thermal effects, and light-matter interactions in systems made of thousands of atoms and of various materials has become critical. In this paper, we review one approach that satisfies all these requirements, the Non-equilibrium Green’s Function (NEGF) formalism, focusing on its combination with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ab initio</i> bandstructure models. The NEGF method allows to treat electrical, thermal, and optical transport at the quantum mechanical level in multi-material, multi-functional devices, without any empirical parameters. Besides advanced logic switches, it can be used to simulate e.g., photo-detectors, thermoelectric generators, or memory cells composed of almost any materials, in the ballistic limit of transport and in the presence of scattering. The key features of NEGF are summarized first, then selected applications are presented, finally challenges and opportunities are discussed.

Toward an extreme-scale electronic structure system.

Electronic structure calculations have the potential to predict key matter transformations for applications of strategic technological importance, from drug discovery to material science and catalysis. However, a predictive physicochemical characterization of these processes often requires accurate quantum chemical modeling of complex molecular systems with hundreds to thousands of atoms. Due to the computationally demanding nature of electronic structure calculations and the complexity of modern high-performance computing hardware, quantum chemistry software has historically failed to operate at such large molecular scales with accuracy and speed that are useful in practice. In this paper, novel algorithms and software are presented that enable extreme-scale quantum chemistry capabilities with particular emphasis on exascale calculations. This includes the development and application of the multi-Graphics Processing Unit (GPU) library LibCChem 2.0 as part of the General Atomic and Molecular Electronic Structure System package and of the standalone Extreme-scale Electronic Structure System (EXESS), designed from the ground up for scaling on thousands of GPUs to perform high-performance accurate quantum chemistry calculations at unprecedented speed and molecular scales. Among various results, we report that the EXESS implementation enables Hartree-Fock/cc-pVDZ plus RI-MP2/cc-pVDZ/cc-pVDZ-RIFIT calculations on an ionic liquid system with 623 016 electrons and 146 592 atoms in less than 45minusing 27 600 GPUs on the Summit supercomputer with a 94.6% parallel efficiency.

The study of ionic conductivity of the Li10GeP2S12 type Solid State Electrolyte by an extrapolation method and a deep-learning method

An extrapolation method is usually applied when Ab initio molecular dynamics (AIMD) is applied to studying ionic conductivity in solid-state electrolytes in lithium-ion batteries. As the ions move slowly in solid-state electrolytes, the first-principles method typically involves computationally intensive calculations, and it can take significant time to obtain accurate results. First-principles method is too expensive for the time scale required at room temperature. The classical molecular dynamics method is typically applied to systems containing thousands of atoms and can simulate the system’s behavior over nanoseconds. During the simulation, the positions and velocities of the atoms are updated at discrete time intervals, allowing the system’s behavior to be studied over time. However, its accuracy depends on the empirical force-field libraries. Limited by the computational resource, the previous studies applied the extrapolation method to obtain the room temperature ionic conductivity, which was not accurate because the linear relationship in the Arrhenius equation was not valid in a wide range of temperatures. Deepmd-Kit is a tool that integrates these two different computational approaches. The extrapolation and Deepmd methods were applied to the materials Li10GeP2S12, Li10SiP2S12, Li10GePS12Cl, and Li10SiPS12Cl, respectively. Both methods showed that the lithium ions favor the c direction when diffusing in the LGPS-type solid-state electrolytes. The ionic conductivity is more accurate with the dependent method compared with experiments.

Chemical Bonding in Large Systems Using Projected Population Analysis from Real-Space Density Functional Theory Calculations.

We present an efficient and scalable computational approach for conducting projected population analysis from real-space finite-element (FE)-based Kohn-Sham density functional theory calculations (DFT-FE). This work provides an important direction toward extracting chemical bonding information from large-scale DFT calculations on materials systems involving thousands of atoms while accommodating periodic, semiperiodic, or fully nonperiodic boundary conditions. Toward this, we derive the relevant mathematical expressions and develop efficient numerical implementation procedures that are scalable on multinode CPU architectures to compute the projected overlap and Hamilton populations. The population analysis is accomplished by projecting either the self-consistently converged FE discretized Kohn-Sham orbitals or the FE discretized Hamiltonian onto a subspace spanned by a localized atom-centered basis set. The proposed methods are implemented in a unified framework within the DFT-FE code where the ground-state DFT calculations and the population analysis are performed on the same FE grid. We further benchmark the accuracy and performance of this approach on representative material systems involving periodic and nonperiodic DFT calculations with LOBSTER, a widely used projected population analysis code. Finally, we discuss a case study demonstrating the advantages of our scalable approach to extract the quantitative chemical bonding information of hydrogen chemisorbed in large silicon nanoparticles alloyed with carbon, a candidate material for hydrogen storage.

RSDFT-NEGF transport simulations in realistic nanoscale transistors

The paper presents a device simulator for computing transport characteristics from first principles. The developed computer program effectively performs large-scale parallel calculation of quasi-one-dimensional quantum transport in realistic nanoscale devices with thousands of atoms in the cross section area of the device channel. Our simulator is based on the real-space Kohn–Sham Hamiltonian in the density functional theory and improved numerical algorithms for reducing computational burden in non-equilibrium Green’s function (NEGF) method. Several computational improvements have been introduced in constructing a reduced quantum transport model from the original Kohn-Sham Hamiltonian and implementing the R-matrix computational scheme in the NEGF simulations.

Black box vs gray box: Comparing GAP and GPrep-DFTB for ruthenium and ruthenium oxide.

The increasing popularity of machine learning (ML) approaches in computational modeling, most prominently ML interatomic potentials, opened possibilities that were unthinkable only a few years ago-structure and dynamics for systems up to many thousands of atoms at an abinitio level of accuracy. Strictly referring to ML interatomic potentials, however, a number of modeling applications are out of reach, specifically those that require explicit electronic structure. Hybrid ("gray box") models based on, e.g., approximate, semi-empirical abinitio electronic structure with the aid of some ML components offer a convenient synthesis that allows us to treat all aspects of a certain physical system on the same footing without targeting a separate ML model for each property. Here, we compare one of these [Density Functional Tight Binding with a Gaussian Process Regression repulsive potential (GPrep-DFTB)] with its fully "black box" counterpart, the Gaussian approximation potential, by evaluating performance in terms of accuracy, extrapolation power, and data efficiency for the metallic Ru and oxide RuO2 systems, given exactly the same training set. The accuracy with respect to the training set or similar chemical motifs turns out to be comparable. GPrep-DFTB is, however, slightly more data efficient. The robustness of GPRep-DFTB in terms of extrapolation power is much less clear-cut for the binary system than for the pristine system, most likely due to imperfections in the electronic parametrization.

Single Non‐Blinking Graphene Quantum Dots Identified by Single‐Particle Catalysis

AbstractQuantum dots (QDs) with non‐blinking applied in bio‐imaging is important for understanding cellular dynamics by monitoring single particles in living cells. Antibunching is once believed to be applied to identify a single QD, which shows a strong dependence on the atom or ion number. However, recent works indicate that the antibunching alone may not be sufficient to demonstrate that the fluorescence comes from a single QD. To some extent, it is neither necessary nor sufficient for a single QD, though it is absolutely right for a single atom because a single QD is composed of thousands of atoms, which in principle are favorable to the formation of multi‐excitonic states. Blinking behavior, almost unobservable in ensembles due to the averaging, is characteristic of the single QD which owns the quantum effect due to its discrete energy levels. Here, a method of transformation of a single graphene quantum dot from non‐blinking to blinking via catalytic reaction to identify a single QD is reported, which can also help with understanding the catalytic dynamics of a single QD, which is a complementary means of confirming a single QD. The reported technique paves the way to understanding a single QD and its catalytic and kinetic behaviors.

Вычисление тензора эффективной массы в ε- и ξ-фосфорене методом теории функционала плотности

In this work, the effective mass tensor was obtained from the law of dispersion of electrons in the conduction band of various allotropic modifications of phosphorene, calculated using the density functional theory. For calculations, we used the quantum chemical modeling package, OpenMX, which significantly reduces the calculation time for systems consisting of hundreds and thousands of atoms. Comparison of the obtained results of tensors for black and blue phosphorene with other works showed the correctness of the methods used, which were subsequently applied to other less studied allotropic modifications of phosphorene. In particular, ε- and ξ-phosphorene were studied, which are characterized by a non-hexagonal crystal lattice with an indirect band gap. This may be of interest from the point of view of discovering interesting acoustic-electronic properties in these materials, for example, resonant absorption of acoustic waves. Parallels between allotropes are drawn. From the point of view of effective mass tensors, ε-phosphorene is isotropic, like blue phosphorene, while ξ-phosphorene, on the contrary, has a non-isotropic structure, like black phosphorene. The results obtained during the study can be used in further study of the physical properties of materials, such as conductivity and photovoltaic effects.

Complexity reduction in density functional theory: Locality in space and energy.

We present recent developments of the NTChem program for performing large scale hybrid density functional theory calculations on the supercomputer Fugaku. We combine these developments with our recently proposed complexity reduction framework to assess the impact of basis set and functional choice on its measures of fragment quality and interaction. We further exploit the all electron representation to study system fragmentation in various energy envelopes. Building off this analysis, we propose two algorithms for computing the orbital energies of the Kohn-Sham Hamiltonian. We demonstrate that these algorithms can efficiently be applied to systems composed of thousands of atoms and as an analysis tool that reveals the origin of spectral properties.

Enabling large-scale quantum path integral molecular dynamics simulations through the integration of Dcdftbmd and i-PI codes.

A large-scale quantum chemical calculation program, Dcdftbmd, was integrated with a Python-based advanced atomistic simulation program, i-PI. The implementation of a client-server model enabled hierarchical parallelization with respect to replicas and force evaluations. The established framework demonstrated that quantum path integral molecular dynamics simulations can be executed with high efficiency for systems consisting of a few tens of replicas and containing thousands of atoms. The application of the framework to bulk water systems, with and without an excess proton, demonstrated that nuclear quantum effects are significant for intra- and inter-molecular structural properties, including oxygen-hydrogen bond distance and radial distribution function around the hydrated excess proton.

Atomically Precise Clusters: Chemical Evolution of Molecular Matter at the Nanoscale

The chemistry of nanoparticles with atomic precision has become a subject of interest due to the unique physical and chemical properties of these systems in comparison to their bulk counterparts. Nanoparticles typically contain thousands of atoms, arranged in a specific fashion. There is a need to bridge the gap between single atoms and nanoparticles to understand the size evolution of matter. This regime, composed of a few atoms, is called nanoclusters having size in the range of 1 to 3 nm, with a precise number of atoms, with well-defined structure and properties. Most of studied materials in this class are composed of noble metals. Various techniques, including UV/vis spectroscopy, mass spectrometry, and single crystal XRD, reveal their molecular nature. Their unique electronic properties can be used for applications in the fields of catalysis, sensing, magnetism, medicinal, bio-imaging, etc. This article touches upon the basics of nanoclusters, their synthesis, characterization, atomic structures, and supramolecular arrangements. We hope that this work provides a brief understanding of nanoclusters and inspires young scientists to conduct research in the chemistry of clusters.