Atomic Environment Research Articles

Graphene/aluminum oxide interfaces for nanoelectronic devices

In this work, we study theoretically and experimentally graphene/aluminum oxide interfaces as 0D/2D interfaces for quantum electronics as the nature of the interface is of paramount importance to understand the quantum transport mechanism. Indeed, the electronic transport is driven either by a channel arising from a strong hybridization at the interface, or by tunneling across a van der Waals interface, with very different electric characteristics. By combining electronic spectroscopy and scanning microscopy with density functional theory calculations, we show that the interface is of weak and van der Waals nature. Quantum transport measurements in a single electron transistor confirm this result. Our results provide a first insight into the interfacial properties van der Waals materials based single electron device, and the key role played by the control of the interface states. The weak van der Waals coupling reported is promising for single electron device, where the control of the environmental charges is known to be a key challenge towards applications. Moreover, the unique vertical device architecture, enabled by the dual role of graphene including its vertical electric field transparency, opens the doors for a new class of single electron devices with higher scaling capability and functionalities. This work paves the way to new atomic environment control in single electron device.

Effect of Irradiation Dose on the Structure and Tribological Performance of Ti:WS2/PFPE Composite Lubricating System in Atomic Oxygen Environment

Perfluoropolyether (PFPE) oil possessing higher oxidation stability than hydrocarbon lubricant is combined with Ti:WS2 film to construct a solid–liquid composite lubricating system. The results indicate that atomic oxygen (AO) irradiation generates some important effects for the Ti:WS2/PFPE composite lubricating system. With an increase of irradiation dose, the molecular structure of PFPE oil experiences carbon chain breakage and appears to be an obvious molecule for cross-linking and polymerization. Meanwhile, Ti:WS2 film also is oxidized to a different extent to form WO3. The changes in the solid–liquid system resulted in greatly different tribological properties of the Ti:WS2/PFPE system. The friction coefficient of the system significantly increases with low-dose irradiation, while inversely, the value sharply decreases with high-dose AO, which is related to the presence of WO3 and formation of a transfer film. Finally, the failure mechanism of Ti:WS2/PFPE system under AO irradiation has been revealed.

Tailoring the Atomic-Local Environment of Carbon Nanotube Tips for Selective H2 O2 Electrosynthesis at High Current Densities.

The atomic-local environment of catalytically active sites plays an important role in tuning the activity of carbon-based metal-free electrocatalysts (C-MFECs). However, the rational regulation of the environment is always impeded by synthetic limitations and insufficient understanding of the formation mechanism of the catalytic sites. Herein, the possible cleavage mechanism of carbon nanotubes (CNTs) through the crossing points during ball-milling is proposed, resulting in abundant CNT tips that are more susceptible to be modified by heteroatoms, achieving precise modulation of the atomic environment at the tips. The obtained CNTs with N,S-rich tips (N,S-TCNTs) exhibit a wide potential window of 0.59V along with H2 O2 selectivity for over 90.0%. Even using air as the O2 source, the flow cell system with N,S-TCNTs catalyst attains high H2 O2 productivity up to 30.37molgcat. -1 h-1 @350mAcm-2 , superior to most reported C-MFECs. From a practical point of view, a solid electrolyzer based on N,S-TCNTs is further employed to realize the in-situ continuous generation of pure H2 O2 solution with high productivity (up to 4.35mmolcm-2 h-1 @300mAcm-2 ; over 300h). The CNTs with functionalized tips hold great promise for practical applications, even beyond H2 O2 generation.

First - Principles study of hydrogen - Carbide interaction in bcc Fe

Rapid developments in the field of hydrogen energy have prompted the need for safe and efficient hydrogen transportation and storage. Steels form the backbone of the current energy infrastructure and thus offer a fast and cost-effective solution. Their excellent mechanical properties are attributed to the underlying microstructure which comprises of finely dispersed nano-precipitates. However, one major factor restricting their application is their susceptibility to Hydrogen Embrittlement (HE). In the past decade, experimental and theoretical works have been carried out to understand if the nano-sized carbides can aid in reducing the susceptibility to HE along with providing strengthening. Within this ab-inito study, we investigated the effectiveness of fully coherent nano-carbides (i.e. TiC, VC and NbC) to limit the diffusible hydrogen content in bcc Fe. Our study revealed that the interplay between hydrogen and carbon vacancies, local atomic environment at interface as well as elastic strain fields at the interface can lead to significantly increased hydrogen solubilities. While in TiC, the deepest traps were found to be in the bulk of carbides, in VC and NbC, the elastic strain fields around the interface led to the strongest trapping. Further, the formation of a two-hydrogen-vacancy complex was found to be favourable in VC. Finally, the migration barriers for hydrogen trapping in bulk TiC as well as across the Fe/TiC coherent interface indicate that these deep traps in the form of carbon vacancies are fairly accessible.

Exploring NaCl-PuCl3 molten salts with machine learning interatomic potentials and graph theory

Actinide molten salts are the basis of the liquid fuels used in molten salt reactors. Due to the inherent difficulties associated with high temperature and hazardous conditions, experimental investigations of fundamental properties of these materials are usually challenging. In this work, we describe the structure and transport of NaCl-PuCl3 mixtures using computational techniques. Three compositions were considered (16, 25, and 36 mol% PuCl3) over a temperature range (730 – 1257 K) using ab initio molecular dynamics, which provided the necessary data sets for training machine learned interatomic potentials. Molecular dynamics simulations based on these potentials were then used to determine structure and transport properties. A substantial change was noted in the structure factor when increasing the PuCl3 content from 25 to 36 mol%. This change is linked to the aggregation of larger Pu3+ clusters. In addition, the similarity of the atomic environments of metal cations in molten salt systems to their solid states counterparts was investigated using an unsupervised learning technique. Finally, graph theory was employed to explore the structure and size of actinide networks. Consistent with the structure factor, a dense Pu3+ intermolecular structure is observed within the 36 mol% PuCl3 mixture. The structure of cation-cation inter-junctions is also discussed. In all cases, the diffusion of Pu3+ is significantly lower than that of Na+ and Cl−.

Neighbors Map: An efficient atomic descriptor for structural analysis

Accurate structural analysis is essential to gain physical knowledge and understanding of atomic-scale processes in materials from atomistic simulations. However, traditional analysis methods often reach their limits when applied to crystalline systems with thermal fluctuations, defect-induced distortions, partial vitrification, etc. In order to enhance the means of structural analysis, we present a novel descriptor for encoding atomic environments into 2D images, based on a pixelated representation of graph-like architecture with weighted edge connections of neighboring atoms. This descriptor is well adapted for Convolutional Neural Networks and enables accurate structural analysis at a low computational cost. In this paper, we showcase a series of applications, including the classification of crystalline structures in distorted systems, tracking phase transformations up to the melting temperature, and analyzing liquid-to-amorphous transitions in pure metals and alloys. This work provides the foundation for robust and efficient structural analysis in materials science, opening up new possibilities for studying complex structural processes, which cannot be described with traditional approaches.

Nanocomposites Based on Polyethylene and Nickel Ferrite: Preparation, Characterization, and Properties.

Composite materials based on NiFe2O4 nanoparticles and polyethylene matrix have been synthesized by thermal decomposition to expand the application area of high-pressure polyethylene by filling it with nanoscale particles. The synthesized compositions were obtained in the form of a dark gray powder and compressed for further study According to TEM, the average particle size in composites was 2, 3, and 4 nm in samples with a filling of 10%, 20% and 30%. The concentration dependences of the specific electrical resistivity ρV, dielectric permittivity ε, saturation magnetization MS and the parameters of reflection and attenuation of microwave power of the obtained composites were investigated. The threshold for percolation in such materials is found to be within a concentration range of 20…30%. The electronic and atomic structure of composites was studied by methods of Mössbauer spectroscopy, X-ray diffraction and X-ray absorption spectroscopy. The closest atomic environment of nickel and iron in nanoparticles is close to that of crystalline NiFe2O4. The dependence of the nanoparticles size as well as the dependence of the number of tetrahedral or octahedral iron positions in nickel ferrite nanoparticles to their content in polyethylene matrix is established. It is shown that composite materials based on NiFe2O4 nanoparticles and polyethylene matrix can be used as components of electromagnetic compatibility systems.

Automated Fragmentation Quantum Mechanical Calculation of 15N and 13C Chemical Shifts in a Membrane Protein.

In this work, we developed an accurate and cost-effective automated fragmentation quantum mechanics/molecular mechanics (AF-QM/MM) method to calculate the chemical shifts of 15N and 13C of membrane proteins. The convergence of the AF-QM/MM method was tested using Krokinobacter eikastus rhodopsin 2 as a test case. When the distance threshold of the QM region is equal to or larger than 4.0 Å, the results of the AF-QM/MM calculations are close to convergence. In addition, the effects of selected density functionals, basis sets, and local chemical environment of target atoms on the chemical shift calculations were systematically investigated. Our results demonstrate that the predicted chemical shifts are more accurate when important environmental factors including cross-protomer interactions, lipid molecules, and solvent molecules are taken into consideration, especially for the 15N chemical shift prediction. Furthermore, with the presence of sodium ions in the environment, the chemical shift of residues, retinal, and retinal Schiff base are affected, which is consistent with the results of the solid-state nuclear magnetic resonance (NMR) experiment. Upon comparing the performance of various density functionals (namely, B3LYP, B3PW91, M06-2X, M06-L, mPW1PW91, OB95, and OPBE), the results show that mPW1PW91 is a suitable functional for the 15N and 13C chemical shift prediction of the membrane proteins. Meanwhile, we find that the improved accuracy of the 13Cβ chemical shift calculations can be achieved by the employment of the triple-ζ basis set. However, the employment of the triple-ζ basis set does not improve the accuracy of the 15N and 13Cα chemical shift calculations nor does the addition of a diffuse function improve the overall prediction accuracy of the chemical shifts. Our study also underscores that the AF-QM/MM method has significant advantages in predicting the chemical shifts of key ligands and nonstandard residues in membrane proteins than most widely used empirical models; therefore, it could be an accurate computational tool for chemical shift calculations on various types of biological systems.

Catalytic CO2 Conversion to C1 Chemicals over Single‐Atom Catalysts

AbstractCarbon dioxide, as an abundant and nontoxic C1 resource, can be extensively applied to produce C1 building blocks via direct and indirect routes. On account of the stable electronic structure and high activation energy of CO2, the most challenging problem of CO2 conversion is the rational design of low‐cost and efficient catalysts with attractive performance. Single‐atom catalysts (SACs) with atomically dispersed metal atoms, strong metal–support interaction, and tunable/unsaturated coordination environment offer a potential choice by achieving maximum atomic utilization and lowering the CO2 activation barrier. Furthermore, the geometric and electronic structure of SACs can be easily regulated by tuning the coordination environment of single atoms, which significantly affects their catalytic performance. In this review, therefore, a comprehensive review of thermocatalytic CO2 conversion to C1 chemicals over SACs is presented. Specifically, the physiochemical and electronic properties, synthesis methods, and characterization technologies are introduced. Thereafter, an overview of catalytic performance and mechanism of SACs is described during thermocatalytic CO2 conversion to C1 chemicals. Finally, the main limitations of current studies on CO2 conversion to C1 chemicals over SACs are summarized, and simultaneouslyperspectives on the future are proposed, in order to provide a guidance for decarbonizing the industries and cycling greenhouse gases.

Manipulating the Spin Orientation of Co Atoms Using Monatomic Cu Chains.

Harnessing the spin of single atoms is at the heart of quantum information nanotechnology based on magnetic concepts. By attaching single Co atoms to monatomic Cu chains, we demonstrate the ability to control the spin orientation by the atomic environment. Due to spin-orbit coupling (SOC), the spin is tilted by ≈58° from the surface normal toward the chain as evidenced by inelastic tunneling spectroscopy. These findings are reproduced by density functional theory calculations and have implications for Co atoms on pristine Cu(111), which are believed to be Kondo systems. Our quantum Monte Carlo calculations suggest that SOC suppresses the Kondo effect of Co atoms at chains and on the flat surface. Our work impacts the fundamental understanding of low-energy excitations in nanostructures on surfaces and demonstrates the ability to manipulate atomic-scale magnetic moments, which can have tremendous implications for quantum devices.

Role of vacancies in structural thermalization of binary and high-entropy alloys

Vacancy assisted atomic self-diffusion is a major structural thermalization mechanism in bulk metal alloys. Depending on alloy composition, the local atomic environments might stabilize vacancies to such extent that the vacancies become trapped and the atomic self-diffusion part of the thermalization process stalls. The consequence is that such alloys get kinetically trapped in disordered structures. In this study, we investigate equimolar AgAu, CuPt, AgPdPtIr, and AgAuCuPdPt alloy thermalizing using Metropolis Monte Carlo simulations in two approaches, one where the alloy structure changes through vacancy migration and one where the structure changes by swapping atomic pairs. By comparing the two approaches, we find that the vacancy is less effective at thermalizing alloys with more elements (i.e. AgPdPtIr and AgAuCuPdPt), more heterogeneous configurational internal energy distributions (i.e. CuPt and AgPdPtIr), and strong interactions between certain elements, e.g. Ir-Ir interactions in AgPdPtIr. In the case of AgPdPtIr, the vacancy cannot thermalize Ir-Ir neighbors even when the vacancy is mobile, because the vacancy has difficulty breaking individual Ir-Ir pairs apart.

Robust crystal structure identification at extreme conditions using a density-independent spectral descriptor and supervised learning

The increased time- and length-scale of classical molecular dynamics simulations have led to raw data flows surpassing storage capacities, necessitating on-the-fly integration of structural analysis algorithms. As a result, algorithms must be computationally efficient, accurate, and stable at finite temperature to reliably extract the relevant features of the data at simulation time. In this work, we leverage spectral descriptors to encode local atomic environments and build crystal structure classification models. In addition to the classical way spectral descriptors are computed, i.e. over a fixed radius neighborhood sphere around a central atom, we propose an extension to make them independent from the material’s density. Models are trained on defect-free crystal structures with moderate thermal noise and elastic deformation, using the linear discriminant analysis (LDA) method for dimensionality reduction and logistic regression (LR) for subsequent classification. The proposed classification model is intentionally designed to be simple, incorporating only a limited number of parameters. This deliberate simplicity enables the model to be trained effectively even when working with small databases. Despite the limited training data, the model still demonstrates inherent transferability, making it applicable to a broader range of scenarios and datasets. The accuracy of our models in extreme conditions (high temperature, high density, large deformation) is compared to traditional algorithms from the literature, namely adaptive common neighbor analysis (a-CNA), polyhedral template matching (PTM) and diamond structure identification (IDS). Finally, we showcase two applications of our method: tracking a solid–solid BCC-to-HCP phase transformation in Zirconium at high pressure up to high temperature, and visualizing stress-induced dislocation loop expansion in single crystal FCC Aluminum containing a Frank–Read source, at high temperature.

Wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows.

Predictive atomistic simulations are increasingly employed for data intensive high throughput studies that take advantage of constantly growing computational resources. To handle the sheer number of individual calculations that are needed in such studies, workflow management packages for atomistic simulations have been developed for a rapidly growing user base. These packages are predominantly designed to handle computationally heavy abinitio calculations, usually with a focus on data provenance and reproducibility. However, in related simulation communities, e.g., the developers of machine learning interatomic potentials (MLIPs), the computational requirements are somewhat different: the types, sizes, and numbers of computational tasks are more diverse and, therefore, require additional ways of parallelization and local or remote execution for optimal efficiency. In this work, we present the atomistic simulation and MLIP fitting workflow management package wfl and Python remote execution package ExPyRe to meet these requirements. With wfl and ExPyRe, versatile atomic simulation environment based workflows that perform diverse procedures can be written. This capability is based on a low-level developer-oriented framework, which can be utilized to construct high level functionality for user-friendly programs. Such high level capabilities to automate machine learning interatomic potential fitting procedures are already incorporated in wfl, which we use to showcase its capabilities in this work. We believe that wfl fills an important niche in several growing simulation communities and will aid the development of efficient custom computational tasks.

Ab initio study of the structure and properties of amorphous silicon hydride from accelerated molecular dynamics simulations

This paper presents a large-scale ab initio simulation study of amorphous silicon hydride (a-Si1-xHx) with an emphasis on the structure and properties of the material across a range of hydrogen concentration by combining accelerated molecular dynamics (MD) simulations with first-principles density-functional calculations. The accelerated MD scheme relied on classical metadynamics, which enabled the development of 2500+ high-quality structural models of a-Si1-xHx, with system sizes ranging from 150 to 6000 atoms and hydrogen concentrations vary from 6 to 20 at.%. The resulting amorphous networks were found to be completely free from any coordination defects and that they all exhibited a pristine band-gap in their electronic spectrum. The microstructural properties of hydrogen distributions were examined with an emphasis on the presence of isolated and clustered environments of hydrogen atoms. The results were compared with experimental data obtained from X-ray diffraction, infrared spectroscopy and nuclear magnetic resonance studies.

Relevance of structural defects to the mechanism of mechanical deformation in metallic glasses

It is known that deformation in disordered materials such as metallic glasses and supercooled liquids occurs via the cooperative rearrangement of atoms or constituent particles at dynamical heterogeneities, commonly regarded as point-like defects. We show via molecular-dynamics simulations that there is no apparent relationship between atomic rearrangements and the local atomic environment as measured by the atomic-level stresses, kinetic and potential energies, and the per-atom Voronoi volume. In addition, there is only a weak correlation between atomic rearrangements and the largest and smallest eigenvalues of the dynamical matrix. Our results confirm the transient nature of dynamical heterogeneities and suggest that the notion of defects may be less relevant than that of a propensity for rearrangement.

Development of an interatomic potential for the W–Ta system

A physics-inspired, data-driven interatomic potential framework for multi-element system is presented. This potential is based on the generalization of the embedded atom method potential and systematically incorporates two-, three- and many-body effects. Different atomic environments are described by atom-centered Gaussian basis sets which provides sufficient flexibility to capture diverse atomic environments and allows for easy optimization of the free parameters. An interatomic potential model for the tungsten–tantalum (W–Ta) system is developed using this framework by training on data from ab initio density functional theory (DFT) calculations. The potential is thoroughly tested at various compositions by comparing bulk and defect properties from experimental and DFT data. It is shown that the potential predicts the elastic constants, defect properties, such as vacancy and interstitial formation energies, core structures of dislocations, and melting points for both pure and tantalum and tungsten with an accuracy comparable to other single element machine learning based potentials. The potential model is used to investigate the formation energies of ordered alloys and vacancy formation energies for chemically random W–Ta alloys.

Tailoring the electronic structure of PtNi nanoalloy for boosting methanol electro-oxidation reaction

Tailoring the electronic structure of Pt-based catalysts can effectively facilitate their methanol oxidation reaction (MOR). Despite a great deal of effort, the fabrication of Pt-based catalysts with both tunable electronic structure and well-dispersion is still challenging. Herein, the PtNi alloy nanoparticles supported on poly (3, 4-ethylenedioxythiophene) modified multi-walled carbon nanotubes (PtNi/PEDOT-MWCNTs) was prepared for MOR. Transmission electron microscopy reveals the successful synthesis of PtNi nanoparticles (3.5 nm) by the reduction of H2. The X-ray photoelectron spectroscopy confirmed the strong electronic interaction between Ni and Pt, and the electronic environment of Pt atoms can be tailored by alloying different atomic content of Ni. Thus, the obtained PtNi/PEDOT-MWCNTs was suitable to design an effective electro-catalyst. As revealed through the electrochemical measurements, the fabricated PtNi/PEDOT-MWCNTs catalyst displayed higher peak current density, better stability, and excellent CO-tolerance than those of Pt nanoparticles dispersed on PEDOT-MWCNTs, MWCNTs, and commercial carbon black. The surface/interface engineering tactic highlights the tailoring of the electronic structure of Pt-based catalysts, which can be applied in various fields, including methanol fuel cells and other catalytic reactions.

Microwave Regenerable Nickel, Zinc Co-doped Nitrogen-Coordinated Porous Carbon Catalyst for Nitrogen Fixation.

More than 90% of the global NH3 synthesis is dominated by the Haber-Bosch process, which consumes 2% of the worldwide energy and generates 1.44% of the global carbon emission. The electrochemical N2 reduction reaction (NRR) is regarded as an attractive alternative route to produce NH3 under mild reaction conditions, but the electrocatalysts suffer from the difficulty of N≡N cleavage. In this work, we report a leaf-like MOF-derived Ni/Zn bimetallic co-doped nitrogen-coordinated porous carbon (Ni/Zn-NPC) as a cost-effective NH3 synthesis electrocatalyst. The resultant electrocatalyst achieved a high NH3 production rate of 22.68 μg h-1 mgcat-1 at -1.0 V vs a reversible hydrogen electrode (RHE) in a 0.1 M Na2SO4 electrolyte. The Ni/Zn-NPC material can be called a microwave regenerable catalyst because microwave treatment has proven to be a crucial part of the multi-field coupling to detoxify and make the catalyst reactive, further improving its stability. Density functional theory (DFT) was chosen to explore the mechanism of Ni/Zn-NPC for NRR, providing a profound prediction of the structure of the active site and related reaction pathways and revealing that trace Ni doping optimizes the local coordination environment and N2 adsorption of Zn atoms.

Grain boundary alloying segregation to resist hydrogen embrittlement in BCC-Fe steels: Atomistic insights into solute-hydrogen interactions

Grain boundary (GB) segregation of alloying elements is widely recognized to influence the mechanical properties of metallic materials. However, the rational utilization of GBs' chemical heterogeneity to mitigate hydrogen embrittlement (HE) remains challenging. Here, we employ comprehensive first-principles calculations to investigate solute-hydrogen interactions across five distinct GBs. The calculations provide fundamental insights into H concentration, transport, and induced decohesion at BCC-Fe Σ5(013)[100] GB with alloying segregations including W, Mo, Nb, and Mn, emphasizing the crucial importance of hydrogen adsorption energies. Through statistically identifying the effective descriptors derived from complex atomic environments of various GBs, we elucidate solute-hydrogen interaction mechanisms at GBs from broadly applicable solute valence and electronegativity factors. The analysis highlights the vital role of concurrently controlling alloying segregation tendency, GB separation work, and local H solubilities, thereby comprehending GB engineering strategies and facilitating design of high-performance alloys with GBs resistant to HE.

In-situ observation of ‘chemical’ strengthening induced by compositional fluctuations in Nb-Mo-Ta-W

The mechanical response of refractory high-entropy alloys (RHEA) can be greatly influenced by local chemical fluctuations. Here, we perform in-situ transmission electron microscopy (TEM) nanocompression tests at room temperature of body-centered cubic Nb-Mo-Ta-W RHEA single-crystal nanopillars to understand the reasons behind this at atomic scale. We find that (i) chemical composition is not uniform in space, following certain oscillatory patterns and (ii) that specimen near-surface regions with local ‘soft’ spots favor dislocation nucleation over ‘hard’ ones. Under stress these dislocations glide inwardly, inevitably running into hard local regions adding hardening stresses of up to 400 MPa to the nominal yield stress. These hard local regions act as ‘chemical’ obstacles as revealed by the occurrence of double cross-slip and the formation of prismatic loops. Using electron dispersive spectroscopy along with atomic resolution TEM, we demonstrate that the obstacles correspond to atomic environments presumably rich in W surrounded by misfit edge dislocations.