Thousands Of Atoms Research Articles

Emergence of ferrromagnetism in vanadium doped aluminium phosphide using density functional theory

The theoretical investigation of the structural, electronic and magnetic properties of Al0.75V0.25P Diluted Magnetic Semiconductors in Zinc Blende (B3) phase has been done using Density Functional Theory (DFT) as implemented in the Spanish Initiative for Electronic Simulations with Thousands of Atoms code using LDA + U as exchange–correlation (XC) potential. The study of electronic structures and magnetic properties show that doping of Vanadium transition metal atom in AlP results into induction of ferromagnetism with 100% spin polarization and develops half metallic character in Al0.75V0.25P ternary alloy. The calculated results of lattice constant, energy band gap and bulk modulus of pure AlP are in agreement with previous results available in literature. The calculated values of band gap and total spin polarization of Al0.75V0.25P are 1.816 eV and 2.0 μB respectively.

Study of effect of hydrostatic pressure on structural, electronic and magnetic properties of In0.75Mn0.25P using density functional theory

In this paper, the investigation of effect of hydrostatic pressure on the structural, electronic and magnetic properties of In0.75Mn0.25P Diluted Magnetic Semiconductor in Zinc Blende (B3) phase at 0 GPa–26 GPa pressure range is done using first principal calculations as implemented in Spanish Initiative for Electronic Simulations with Thousands of Atoms (SIESTA) code.The theoretical investigation of electronic and magnetic properties of this compound represents that the compound is half-metallic ferromagnet and show 100% spin polarization at different values of pressure. It is found that forbidden energy band gap increases with increase in pressure as spin polarized band structures experience changes with applied pressure. The calculated results show that lattice constant and volume of compound decreases along-with increase in induced local magnetic moments values of non-magnetic indium and phosphorus atoms with applied pressure.

Quantum engineered Kondo lattices

Atomic manipulation techniques have provided a bottom-up approach to investigating the unconventional properties and complex phases of strongly correlated electron materials. By engineering artificial systems containing tens to thousands of atoms with tailored electronic or magnetic properties, it has become possible to explore how quantum many-body effects emerge as the size of a system is increased from the nanoscale to the mesoscale. Here we investigate both theoretically and experimentally the quantum engineering of nanoscale Kondo lattices – Kondo droplets – exemplifying nanoscopic replicas of heavy-fermion materials. We demonstrate that by changing a droplet’s real-space geometry, we can not only create coherently coupled Kondo droplets whose properties asymptotically approach those of a quantum-coherent Kondo lattice, but also markedly increase or decrease the droplet’s Kondo temperature. Furthermore we report on the discovery of a new quantum phenomenon – the Kondo echo – a signature of droplets containing Kondo holes functioning as direct probes of spatially extended, quantum-coherent Kondo cloud correlations.

Local Algorithms for Minimizing the Force Field for 3D Representation of Macromolecules

The majority of problems in structural computational biology require minimization of the energy function (force field) defined on the molecule geometry. This makes it possible to determine properties of molecules, predict the correct arrangement of protein chains, find the best molecular docking for complex formation, verify hypotheses concerning the protein design, and solve other problems arising in modern drug development. In the case of low-molecular compounds (consisting of less than 250 atoms), the problem of finding the geometry that minimizes the energy function is well studied. The minimization of macromolecules (in particular, proteins) consisting of tens of thousands of atoms is more difficult. However, a distinctive feature of statements of these problems is that initial approximations that are close to the solution are often available. Therefore, the original problem can be formulated as a problem of nonconvex optimization in the space of about $${{10}^{4}}$$ variables. The complexity of computing the function and its gradient is quadratic in the number variables. A comparative analysis of gradient-free methods with gradient-type methods (gradient descent, fast gradient descent, conjugate gradient, and quasi-Newton methods) in their GPU implementations is carried out.

Combining Embedded Mean-Field Theory with Linear-Scaling Density-Functional Theory.

We demonstrate the capability of embedded mean-field theory (EMFT) within the linear-scaling density-functional-theory code ONETEP, which enables DFT-in-DFT quantum embedding calculations on systems containing thousands of atoms at a fraction of the cost of a full calculation. We perform simulations on a wide range of systems from molecules to complex nanostructures to demonstrate the performance of our implementation with respect to accuracy and efficiency. This work paves the way for the application of this class of quantum embedding method to large-scale systems that are beyond the reach of existing implementations.

Coupled experimental-computational analysis of primary static recrystallization in low carbon steel

Primary static recrystallization is a restoration process during which the critically strong deformed microstructure is atomistically reconstructed into a polycrystal with orders of magnitude lower defect density. Advances in diffraction methods catalyzed research activities directed towards more accurate understanding of recrystallization. While these efforts rendered most mechanisms by now qualitatively well understood, many quantitative details remain still unknown. Computer simulations are a viable option to provide qualitative insights into the complex recrystallization process as they provide unlimited observability. However, simulation tools for studying recrystallization in volumes that are significantly large enough for making predictions of mean-field descriptors, such as the distribution of grain sizes or texture evolution, are typically based on continuum models. The use of such models requires to accept certain assumptions on how the collective behavior of multiple thousands of atoms can be homogenized. One aspect of special importance for continuum models is the correct prediction of the nucleation process as it influences virtually all quantitative descriptors of the recrystallized microstructure. This study presents a one-to-one comparison of simulation results to quasi in situ scanning electron microcopy/electron backscatter diffraction results revealing how two different assumptions for the crystallographic orientation of the nuclei perform in reproducing the experimentally observed recrystallization microstructure. Moreover, by comparing the recrystallized microstructure at the surface and in the interior of the three-dimensional model, it is shown how quasi in situ experiments systematically underestimate the recrystallization rate and predict a distorted grain size distribution.

Using density functional calculations to elucidate atomic ordering of Pd-Rh nanoparticles at sizes relevant for catalytic applications

Pd-Rh nanoparticles are known to easily undergo surface restructuring in reactive environment. This study quantifies, with the help of density functional (DFT) calculations and a novel topological approach, atomic ordering and surface segregation effects in Pd-Rh particles with compositions 1:3, 1:1 and 3:1 containing up to 201 atoms (ca. 1.7 nm). The obtained data are used to reliably optimise energetically preferred atomic orderings in inaccessible by DFT Pd-Rh particles containing thousands of atoms and exhibiting sizes exceeding 5 nm, which are typical for catalytic metal particles. It is outlined, how segregation effects on the surface arrangement of Pd-Rh nanoalloy catalysts induced by adsorbates can be evaluated in a simple way within the present modelling setup. Catalytically active Pd-Rh nanoparticles adapt their surface to chemical environment. Density functional modelling identifies surface composition of Pd-Rh particles containing thousands atoms and paves the way for evaluating surface segregation caused by adsorbed reactants.

Packing Changes in Melting, Freezing, and Coalescence of Titanium Nanoparticles from Atomic Simulations

Atomic simulations by using an embedded-atom-method potential were used to study changes of packing patterns in melting, freezing, and coalescence of titanium particles that contained tens to thousands of atoms. The packing evolution under dynamics processes leads to shape fluctuations. Small particles prefer icosahedron configurations. Large particles undergo hexagonal close-packed (HCP)–body-centered cubic (BCC) and BCC-melt transitions in a heating–cooling cycle. Calculations of specific heat are higher than those predicted from the classical Dulong–Petit law. Upon cooling, the hysteresis transition temperatures depend on the particle size and surface morphologies. The connection at room temperature results from contact and deformation between near facets of two particles. Complex facets exist after coalescence. A single-domain structure occurs for the coalescence of two relatively small particles. The occurrence accompanies the HCP–BCC transition, and the melting temperature is improved. In these large particles, coalescence particles consist of domains before melting.

Semilocal exchange-correlation potentials for solid-state calculations: Current status and future directions

Kohn-Sham (KS) density functional theory (DFT) is a very efficient method for calculating various properties of solids as, for instance, the total energy, the electron density, or the electronic band structure. The KS-DFT method leads to rather fast calculations; however, the accuracy depends crucially on the chosen approximation for the exchange and correlation (xc) functional Exc and/or potential vxc. Here, an overview of xc methods to calculate the electronic band structure is given, with the focus on the so-called semilocal methods that are the fastest in KS-DFT and allow to treat systems containing up to thousands of atoms. Among them, there is the modified Becke-Johnson potential that is widely used to calculate the fundamental bandgap of semiconductors and insulators. The accuracy for other properties like magnetic moment or electron density, that are also determined directly by vxc, is also discussed.

Classical molecular dynamics on graphics processing unit architectures

AbstractMolecular dynamics (MD) has experienced a significant growth in the recent decades. Simulating systems consisting of hundreds of thousands of atoms is a routine task of computational chemistry researchers nowadays. Thanks to the straightforwardly parallelizable structure of the algorithms, the most promising method to speed‐up MD calculations is exploiting the large‐scale processing power offered by the parallel hardware architecture of graphics processing units or GPUs. Programming GPUs is becoming easier with general‐purpose GPU computing frameworks and higher levels of abstraction. In the recent years, implementing MD simulations on graphics processors has gained a large interest, with multiple popular software packages including some form of GPU‐acceleration support. Different approaches have been developed regarding various aspects of the algorithms, with important differences in the specific solutions. Focusing on published works in the field of classical MD, we describe the chosen implementation methods and algorithmic techniques used for porting to GPU, as well as how recent advances of GPU architectures will provide even more optimization possibilities in the future.This article is characterized under: Software > Simulation Methods Computer and Information Science > Computer Algorithms and Programming Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods

DNA-Conjugated Gold Nanoparticles as High-Mass Probes in Imaging Mass Cytometry.

We employ imaging mass cytometry (IMC) to investigate in vitro uptake and cellular distribution of DNA-functionalized gold nanoparticles (AuNPs). IMC enables the multiparametric imaging of cell components and allows for the detection of AuNPs in cells with >100 times higher sensitivity than conventional confocal fluorescence imaging, as each nanoparticle contains thousands of atoms for signal amplification. Changes in the accumulation of nanoparticles in cells due to oligonucleotide sequence-dependent interactions are exploited to examine a model biomarker for hypoxia-microRNA-210. We find that AuNPs functionalized with microRNA-210-targeting sequence accumulate in hypoxic cells 3 to 4-fold compared to normoxic cells. The work examines the potential use of DNA-AuNP as high-mass probes for the analysis of nonabundant nucleic acids.

Complex magnetism of B20-MnGe: from spin-spirals, hedgehogs to monopoles

B20 compounds are the playground for various non-trivial magnetic textures such as skyrmions, which are topologically protected states. Recent measurements on B20-MnGe indicate no clear consensus on its magnetic behavior, which is characterized by the presence of either spin-spirals or three-dimensional objects interpreted to be a cubic lattice of hedgehogs and anti-hedgehogs. Utilizing a massively parallel linear scaling all-electron density functional algorithm, we find from full first-principles simulations on cells containing thousands of atoms that upon increase of the compound volume, the state with lowest energy switches across different magnetic phases: ferromagnetic, spin-spiral, hedgehog and monopole.

Ab initio electronic structure calculations using a real-space Chebyshev-filtered subspace iteration method

Ab initio electronic structure calculations within Kohn–Sham density functional theory requires a solution for the Kohn–Sham equation. However, the traditional self-consistent field (SCF) approach of solving the equation using iterative diagonalization exhibits an inherent cubic scaling behavior and becomes prohibitive for large systems. The Chebyshev-filtered subspace iteration (CheFSI) method holds considerable promise for large-system calculations by substantially accelerating the SCF procedure. Here, we employed a combination of the real space finite-difference formulation and CheFSI to solve the Kohn–Sham equation, and implemented this approach in ab initio Real-space Electronic Structure (ARES) software in a multi-processor, parallel environment. An improved scheme was proposed to generate the initial subspace of Chebyshev filtering in ARES efficiently, making it suitable for large-scale simulations. The accuracy, stability, and efficiency of the ARES software were illustrated by simulations of large-scale crystalline systems containing thousands of atoms.

Past\u2013future information bottleneck for sampling molecular reaction coordinate simultaneously with thermodynamics and kinetics

The ability to rapidly learn from high-dimensional data to make reliable bets about the future is crucial in many contexts. This could be a fly avoiding predators, or the retina processing gigabytes of data to guide human actions. In this work we draw parallels between these and the efficient sampling of biomolecules with hundreds of thousands of atoms. For this we use the Predictive Information Bottleneck framework used for the first two problems, and re-formulate it for the sampling of biomolecules, especially when plagued with rare events. Our method uses a deep neural network to learn the minimally complex yet most predictive aspects of a given biomolecular trajectory. This information is used to perform iteratively biased simulations that enhance the sampling and directly obtain associated thermodynamic and kinetic information. We demonstrate the method on two test-pieces, studying processes slower than milliseconds, calculating free energies, kinetics and critical mutations.

Analytic QM/MM atomic charge derivatives avoiding the scaling of coupled perturbed equations with the MM subsystem size.

In electrostatic embedding mixed quantum and molecular mechanics (QM/MM) approaches, the QM charge distribution is polarized by the electrostatic interaction with the MM environment. Analytic derivatives of expectation values of operators are required to extract properties such as vibrational spectra. These derivatives usually require solving a set of coupled perturbed equations for each nucleus/atom in the system, thus becoming prohibitive when the MM subsystem contains thousands of atoms. In the context of Electrostatic Potential Fitting (ESPF) QM/MM, we can easily overcome this bottleneck by defining a set of auxiliary coupled perturbed equations called the Q-vector equations. The Q-vector method scales only with the size of the QM subsystem, producing an effective charge tensor that leads to the atomic charge derivative after contraction with the MM electrostatic potential gradient. As an example, we use the charge derivatives as an analysis tool to identify the most important chromophore-polarizing amino-acids in plant cryptochrome. This finding opens up the route of defining polarizable force fields and simulating vibrational spectroscopy using ESPF QM/MM electrostatic embedding at an affordable computational cost.

Oriented Polar Molecules Trapped in Cold Helium Nanodropets: Electrostatic Deflection, Size Separation, and Charge Migration.

Helium nanodroplets doped with polar molecules are studied by electrostatic deflection. This broadly applicable method allows even polyatomic molecules to attain subkelvin temperatures and nearly full orientation in the field. The resulting intense force from the field gradient strongly deflects even droplets with tens of thousands of atoms, the most massive neutral systems studied by beam "deflectometry." We use the deflections to extract droplet size distributions. Moreover, since each host droplet deflects according to its mass, spatial filtering of the deflected beam translates into size filtering of neutral fragile nanodroplets. As an example, we measure the dopant ionization probability as a function of droplet radius and determine the mean free path for charge hopping through the helium matrix. The technique will enable separation of doped and neat nanodroplets and size-dependent spectroscopic studies.

Structure Optimisation of Large Transition‐Metal Complexes with Extended Tight‐Binding Methods

AbstractLarge transition‐metal complexes are used in numerous areas of chemistry. Computer‐aided theoretical investigations of such complexes are limited by the sheer size of real systems often consisting of hundreds to thousands of atoms. Accordingly, the development and thorough evaluation of fast semi‐empirical quantum chemistry methods that are universally applicable to a large part of the periodic table is indispensable. Herein, we report on the capability of the recently developed GFNn‐xTB method family for full quantum‐mechanical geometry optimisation of medium to very large transition‐metal complexes and organometallic supramolecular structures. The results for a specially compiled benchmark set of 145 diverse closed‐shell transition‐metal complex structures for all metals up to Hg are presented. Further the GFNn‐xTB methods are tested on three established benchmark sets regarding reaction energies and barrier heights of organometallic reactions.

Structure Optimisation of Large Transition-Metal Complexes with Extended Tight-Binding Methods.

Large transition-metal complexes are used in numerous areas of chemistry. Computer-aided theoretical investigations of such complexes are limited by the sheer size of real systems often consisting of hundreds to thousands of atoms. Accordingly, the development and thorough evaluation of fast semi-empirical quantum chemistry methods that are universally applicable to a large part of the periodic table is indispensable. Herein, we report on the capability of the recently developed GFNn-xTB method family for full quantum-mechanical geometry optimisation of medium to very large transition-metal complexes and organometallic supramolecular structures. The results for a specially compiled benchmark set of 145 diverse closed-shell transition-metal complex structures for all metals up to Hg are presented. Further the GFNn-xTB methods are tested on three established benchmark sets regarding reaction energies and barrier heights of organometallic reactions.

Scalable Indirect Free Energy Method Applied to Divalent Cation-Metalloprotein Binding.

Many biological processes are based on molecular recognition between highly charged molecules such as nucleic acids, inorganic ions, charged amino acids, etc. For such cases, it has been demonstrated that molecular simulations with fixed partial charges often fail to achieve experimental accuracy. Although incorporation of more advanced electrostatic models (such as multipoles, mutual polarization, etc.) can significantly improve simulation accuracy, it increases computational expense by a factor of 5-20×. Indirect free energy (IFE) methods can mitigate this cost by modeling intermediate states at fixed-charge resolution. For example, an efficient "reference" model such as a pairwise Amber, CHARMM, or OPLS-AA force field can be used to derive an initial estimate, followed by thermodynamic corrections to a more advanced "target" potential such as the polarizable AMOEBA model. Unfortunately, all currently described IFE methods encounter difficulties reweighting more than ∼50 atoms between resolutions due to extensive scaling of both the magnitude of the thermodynamic corrections and their statistical uncertainty. We present an approach called "simultaneous bookending" (SB) that is fundamentally different from existing IFE methods based on a tunable sampling approximation, which permits scaling to thousands of atoms. SB is demonstrated on the relative binding affinity of Mg2+/Ca2+ to a set of metalloproteins with up to 2972 atoms, finding no statistically significant difference between direct AMOEBA results and those from correcting Amber to AMOEBA. The ability to change the resolution of thousands of atoms during reweighting suggests the approach may be applicable in the future to protein-protein binding affinities or nucleic acid thermodynamics.

Scalable atomistic simulations of quantum electron transport using empirical pseudopotentials

The simulation of charge transport in ultra-scaled electronic devices requires the knowledge of the atomic configuration and the associated potential. Such “atomistic” device simulation is most commonly handled using a tight-binding approach based on a basis-set of localized orbitals. Here, in contrast to this widely-used tight-binding approach, we formulate the problem using a highly accurate plane-wave representation of the atomic (pseudo)-potentials. We develop a new approach that separately deals with the intrinsic Hamiltonian, containing the potential due to the atomic configuration, and the extrinsic Hamiltonian, related to the external potential. We realize efficient performance by implementing a finite-element like partition-of-unity approach combining linear shape functions with Bloch-wave enhancement functions. We match the performance of previous tight-binding approaches, while retaining the benefits of a plane wave based model. We present the details of our model and its implementation in a full-fledged self-consistent ballistic quantum transport solver. We demonstrate our implementation by simulating the electronic transport and device characteristics of a graphene nanoribbon transistor containing more than 2000 atoms. We analyze the accuracy, numerical efficiency and scalability of our approach. We are able to speed up calculations by a factor of 100 compared to previous methods based on plane waves and envelope functions. Furthermore, our reduced basis-set results in a significant reduction of the required memory budget, which enables devices with thousands of atoms to be simulated on a personal computer.