Atomic Model Research Articles

Effect of Hydrogen Content on the Microstructure, Mechanical Properties, and Fracture Mechanism of Low-Carbon Lath Martensite Steel

The effect of hydrogen content on the microstructure, mechanical properties, and fracture mechanisms of low-carbon lath martensitic steel was investigated using both experimental methods and atomistic modeling. Tensile testing revealed a transition in the fracture behavior with increases in hydrogen concentration. Specifically, at a hydrogen content of 0.44 wppm, a shift from transgranular to intergranular fractures was observed. The most probable cause of hydrogen embrittlement was identified to be HELP-mediated HEDE. As the hydrogen concentration increased, the dislocation density in close-packed planes, such as (111) and (100), was found to rise. The key differences between the hydrogen-free and hydrogen-charged specimens were the localization and density of dislocations, as well as the change in the distribution of slip bands. Atomistic modeling supported these experimental findings, showing that “quasi-cleavage” cracks predominantly initiate at block boundaries with higher local hydrogen accumulation. These results underscore the significant role of hydrogen in modifying both the microstructural characteristics and fracture behavior of low-carbon martensitic steel, with important implications for its performance in hydrogen-rich environments.

Neural network potential for dislocation plasticity in ceramics

Dislocations in ceramics are increasingly recognized for their promising potential in applications such as toughening intrinsically brittle ceramics and tailoring functional properties. However, the atomistic simulation of dislocation plasticity in ceramics remains challenging due to the complex interatomic interactions characteristic of ceramics, which include a mix of ionic and covalent bonds, and highly distorted and extensive dislocation core structures within complex crystal structures. These complexities exceed the capabilities of empirical interatomic potentials. Therefore, constructing neural network potentials (NNPs) emerges as the optimal solution. Yet, creating a training dataset that includes dislocation structures proves difficult due to the complexity of their core configurations in ceramics and the computational demands of density functional theory for large atomic models containing dislocation cores. In this work, we propose a training dataset from properties that are easier to compute via high-throughput calculation. Using this dataset, we have successfully developed NNPs for dislocation plasticity in ceramics, specifically for three typical functional ceramics: ZnO, GaN, and SrTiO3. These NNPs effectively capture the nonstoichiometric and charged core structures and slip barriers of dislocations, as well as the long-range electrostatic interactions between charged dislocations. The effectiveness of this dataset was further validated by measuring the similarity and uncertainty across snapshots derived from large-scale simulations, alongside extensive validation across various properties. Utilizing the constructed NNPs, we examined dislocation plasticity in ceramics through nanopillar compression and nanoindentation, which demonstrated excellent agreement with experimental observations. This study provides an effective framework for constructing NNPs that enable the detailed atomistic modeling of dislocation plasticity, opening new avenues for exploring the plastic behavior of ceramics.

Optical constants of magnetron sputtered aluminum in the range 17–1300 eV with improved accuracy and ultrahigh resolution in the L absorption edge region

This work determines a new set of EUV/x-ray optical constants for aluminum (Al), one of the most important materials in science and technology. Absolute photoabsorption (transmittance) measurements in the 17–1300 eV spectral range were performed on freestanding Al films protected by carbon (C) layers, to prevent oxidation. The dispersive portion of the refractive index was obtained via the Kramers–Kronig transformation. Our data provide significant improvements in accuracy compared to previously tabulated values and reveal fine structure in the Al L1 and L2,3 regions, with photon energy step sizes as small as 0.02 eV. The implications of this work in the successful realization of EUV/x-ray instruments and in the validation of atomic and molecular physics models are also discussed.

Evaluating the diffusion of Kr in UO2 and ADOPTTM using time-of-flight elastic recoil detection analysis (ToF-erda)

ABSTRACT A combination of 300 keV 84Kr ion implantation and Time-of-Flight Elastic Recoil Detection Analysis is utilised to investigate the diffusion of Kr in UO2 and ADOPTTM fuels. Composition depth-profiles on the nanometer scale were obtained, both for as-implanted samples and after annealing at 800°C for 1 h. Observed drifts in the 84Kr profiles could be associated with short-range diffusion mechanisms. The approach employed here provides the possibility to make direct comparisons with atomistic scale modelling data, and can be of service as a separate effect test in line with the Accelerated Fuel Qualification initiative.

Organic Communities, Atomistic Societies and Loneliness

The article distinguishes two models of human organization: the organic community and the atomistic society. It maintains that the organic paradigm stresses (a) the ideal unity of the whole; (b) internal relations; (c) teleological or dialectical processes; (d) co- or inter-dependent members (e. g. the human body or face); (e) a role-orientation; (f) living functions; (g) freedom defined as doing as you should; and (h) qualitative factors prevail. By contrast, the atomistic model emphasizes (a) the value of individual freedom; (b) external relations; (c) mechanical or causal explanations; (d) separate “parts”(e.g. a machine); (e) a rule orientation; (f) formal, legal, or artificial frameworks; (g) freedom defined as doing as you please; and (h) quantitative factors prevail. The article contends that the sense of individual loneliness or alienation experienced is generally much greater in the atomistic society. And since both the American family and its surrounding society are, in the main, atomisticly-structured, it follows that loneliness is more pronounced and prevalent in American society. The article concludes by offering some programmatic ideals and measures to reverse and mitigate the present tendency toward increasing loneliness in the United States.

Variants of Surface Charges and Capacitances in Electrocatalysis: Insights from Density-Potential Functional Theory Embedded with an Implicit Chemisorption Model

Prevalent electrolyte effects across a wide range of electrocatalytic reactions underscore the general importance of the local reaction conditions in the electrical double layer (EDL). Compared to traditional EDLs, the electrocatalytic siblings feature partially charged chemisorbates that could blur our long-held views of surface charge densities and differential capacitances—two interrelated quantities shaping the crucial local reaction conditions. Herein, five variants of surface charge density and three variants of differential capacitance in the presence of chemisorbates are defined and compared. A semiclassical model of electrocatalytic EDLs is developed for a quantitative analysis of the differences and interrelationships between these variants of surface charge densities and differential capacitances. It is revealed that the potential- and concentration-dependent net charge on these chemisorbates dramatically changes the surface charge densities and differential capacitances. Specifically, the free surface charge density could decrease as the electrode potential becomes more positive, implying that the . The relationship between the free and total surface charge densities is analyzed with aid of the concept of electrosorption valency. By linking the electrosorption valency with the differential capacitance in the absence of chemisorbates, we explain the potential and concentration dependence of the former. The conceptual analysis presented in this work has important implications for experimental characterization and first-principles-based atomistic simulations of electrocatalysis and EDL effects. Particularly, we disclose a hidden yet potentially crucial disadvantage of widely employed atomistic simulation models that fix the coverage of chemisorbates. Proposing the self-consistent implicit model as an expedient remedy for this disadvantage, this work contributes to more realistic modeling of electrocatalytic EDLs. Published by the American Physical Society 2024

Identifying the minimal sets of distance restraints for FRET-assisted protein structural modeling.

Proteins naturally occur in crowded cellular environments and interact with other proteins, nucleic acids, and organelles. Since most previous experimental protein structure determination techniques require that proteins occur in idealized, non-physiological environments, the effects of realistic cellular environments on protein structure are largely unexplored. Recently, Förster resonance energy transfer (FRET) has been shown to be an effective experimental method for investigating protein structure in vivo. Inter-residue distances measured in vivo can be incorporated as restraints in molecular dynamics (MD) simulations to model protein structural dynamics in vivo. Since most FRET studies only obtain inter-residue separations for a small number of amino acid pairs, it is important to determine the minimum number of restraints in the MD simulations that are required to achieve a given root-mean-square deviation (RMSD) from the experimental structural ensemble. Further, what is the optimal method for selecting these inter-residue restraints? Here, we implement several methods for selecting the most important FRET pairs and determine the number of pairs that are needed to induce conformational changes in proteins between two experimentally determined structures. We find that enforcing only a small fraction of restraints, , where is the number of amino acids, can induce the conformational changes. These results establish the efficacy of FRET-assisted MD simulations for atomic scale structural modeling of proteins in vivo.

Impact of Image Content on Medical Crowdfunding Success: A Machine Learning Approach.

As crowdfunding sites proliferate, visual content often serves as the initial bridge connecting a project to its potential backers, underscoring the importance of image selection in effectively engaging an audience. This paper aims to explore the relationship between images and crowdfunding success in cancer-related crowdfunding projects. We used the Alibaba Cloud platform to detect individual features in images. In addition, we used the Recognize Anything Model to label images and obtain content tags. Furthermore, the discourse atomic topic model was used to generate image topics. After obtaining the image features and image content topics, we built regression models to investigate the factors that influence the results of crowdfunding success. Images with a higher proportion of young people (β=0.0753; P<.001), a larger number of people (β=0.00822; P<.001), and a larger proportion of smiling faces (β=0.0446; P<.001) had a higher success rate. Image content related to good things and patient health also contributed to crowdfunding success (β=0.082, P<.001; and β=0.036, P<.001, respectively). In addition, the interaction between image topics and image characteristics had a significant effect on the final fundraising outcome. For example, when smiling faces are considered in conjunction with the image topics, using more smiling faces in the rest and play theme increased the amount of money raised (β=0.0152; P<.001). We also examined causality through a counterfactual analysis, which confirmed the influence of the variables on crowdfunding success, consistent with the results of our regression models. In the realm of web-based medical crowdfunding, the importance of uploaded images cannot be overstated. Image characteristics, including the number of people depicted and the presence of youth, significantly improve fundraising results. In addition, the thematic choice of images in cancer crowdfunding efforts has a profound impact. Images that evoke beauty and resonate with health issues are more likely to result in increased donations. However, it is critical to recognize that reinforcing character traits in images of different themes has different effects on the success of crowdfunding campaigns.

Active learning-based automated construction of Hamiltonian for structural phase transitions: a case study on BaTiO3

The effective Hamiltonians have been widely applied to simulate the phase transitions in polarizable materials, with coefficients obtained by fitting to accurate first-principles calculations. However, it is tedious to generate distorted structures with symmetry constraints, in particular when high-ordered terms are considered. In this work, we implement and apply a Bayesian optimization-based approach to sample potential energy surfaces, automating the effective Hamiltonian construction by selecting distorted structures via active learning. Taking BaTiO3(BTO) as an example, we demonstrate that the effective Hamiltonian can be obtained using fewer than 30 distorted structures. Follow-up Monte Carlo simulations can reproduce the structural phase transition temperatures of BTO, comparable to experimental values with an error<10%. Our approach can be straightforwardly applied on other polarizable materials and paves the way for quantitative atomistic modelling of diffusionless phase transitions.

Skyrmion soliton motion on periodic substrates by atomistic and particle based simulations

Abstract We compare the dynamical behavior of magnetic skyrmions interacting with square and triangular defect arrays just above commensuration using both atomistic and particle-based models. Under applied drives, the initial motion is a kink traveling through the pinned skyrmion lattice. For the square defect array, both models agree well and show a regime in which the soliton motion is locked along 45°. The atomistic model also produces locking of a soliton along 30°, which is absent in the particle-based model. For the triangular defect array, the atomistic model exhibits 30° soliton motion over a wide region of external current values. In contrast, the particle-based model gives 45° soliton motion over a small range of external driving force values. The difference arises because the nondeforming particle model facilitates meandering skyrmion orbits while the deformable atomistic model enables stronger skyrmion-skyrmion interactions that reduce the meandering. Our results indicate that soliton motion through pinned skyrmion lattices on a periodic substrate is a robust effect.

Interaction of complex particles: A framework for the rapid and accurate approximation of pair potentials using neural networks

Motivated by the limitations of conventional coarse-grained molecular dynamics for simulation of large systems of nanoparticles and the challenges in efficiently representing general pair potentials for rigid bodies, we present a method for approximating general rigid body pair potentials based on a specialized type of deep neural network that maintains essential properties, such as conservation of energy and invariance to the chosen origins of the particles. The network uses a specialized geometric abstraction layer to convert the relative coordinates of the rigid bodies to input more suitable to a conventional artificial neural network, which is trained together with the specialized layer. This results in geometric representations of the particles optimized for the specific potential. The network can be trained directly on scalar values to fit a model without explicit gradient and then used to efficiently evaluate the force and torque on the particles resulting from the potential. The concept is demonstrated with an atomistic interaction model for carbon nanotubes and the resulting model is compared with a common type of coarse-grained model optimized for the same potential, with even very small networks comparing favourably and larger networks achieving up to two orders of magnitude lower cost. The sensitivity to noise in the training data is investigated and the model is found to strongly reject noise up to 12.5% given a dataset of 107 samples. The performance of a proof-of-concept implementation is demonstrated on a variety of hardware, showing the models viability for large-scale simulations. Furthermore, generalization to soft bodies and potentials for polydisperse systems are discussed. Published by the American Physical Society 2024

Unveiling Nucleosome Dynamics: A Comparative Study Using All-Atom and Coarse-Grained Simulations Enhanced by Principal Component Analysis.

This study investigates nucleosome dynamics using both all-atom and coarse-grained (CG) molecular dynamics simulations, focusing on the SIRAH force field. Simulations are performed for two nucleosomal DNA sequences, ASP and Widom-601, over six microseconds at physiological salt concentrations. Comparative analysis of structural parameters, such as groove widths and base pair geometries, reveals good agreement between atomistic and CG models, though CG simulations exhibit broader conformational sampling and greater breathing motion of DNA ends. Principal component analysis (PCA) is applied to DNA structural parameters, revealing multiple free energy minima, especially in CG simulations. These findings highlight the potential of the SIRAH CG force field for studying large-scale nucleosome dynamics, offering insights into DNA repositioning and sequence-dependent behavior.

Atomistic Modeling of Natural Gas Desulfurization Process Using Task-Specific Deep Eutectic Solvents Supported by Graphene Oxide.

This study employs Density Functional Theory (DFT) calculations and traditional all-atom Molecular Dynamics (MD) simulations to reveal atomistic insights into a task-specific Deep Eutectic Solvent (DES) supported by graphene oxide with the aim of mimicking its application in the natural gas desulfurization process. The DES, composed of N,N,N',N'-tetramthyl-1,6-hexane diamine acetate (TMHDAAc) and methyldiethanolamine (MDEA) supported by graphene oxide, demonstrates improved efficiency in removing hydrogen sulfide from methane. Optimized structure and HOMO-LUMO orbital analyses reveal the distinct spatial arrangements and interactions between hydrogen sulfide, methane, and DES components, highlighting the efficacy of the DES in facilitating the separation of hydrogen sulfide from methane through DFT calculations. The radial distribution function (RDF) and interaction energies, as determined by traditional all-atom MD simulations, provide insights into the specificity and strength of the interactions between the DES components supported by graphene oxide and hydrogen sulfide. Importantly, the stability of the DES structure supported by graphene oxide is maintained after mixing with the fuel, ensuring its robustness and suitability for prolonged desulfurization processes, as evidenced by traditional all-atom MD simulation results. These findings offer crucial insights into the molecular-level mechanisms underlying the desulfurization of natural gas, guiding the design and optimization of task-specific DESs supported by graphene oxide for sustainable and efficient natural gas purification.

Predicting the Antifungal Activity of Small Organic Compounds on Aspergillus niger Mold using Molecular Dynamics Simulations.

Atomistic models of the plasma membrane of the pathogenic mold Aspergillus niger are developed. These models are described with an empirical molecular mechanical (MM) force field in combination with molecular dynamics (MD) simulations. The solvated plasma membrane models are brought into contact with 35 small organic compounds to observe their impact on a variety of membrane properties. All compounds are added at a constant total mass of 1% of the membrane mass. In addition, the ability of these compounds to inhibit the pathogenic cell growth of mold has been measured. Diffusion of compounds into the membrane model is readily observed during MD simulations. Changes in membrane properties found in simulations are not found to correlate with measured antifungal activities of compounds, suggesting that MD simulations of up to 1 μs are not sufficiently long to adequately describe compound-induced membrane disruption. However, properties related to the position and orientation of compounds relative to the membrane surface as well as hydrogen bonds formed between the compounds and the membrane show clear trends that correlate well with measured activities. A combination of these properties enables an activity prediction of compounds in good agreement with measurements. Activity is found predominantly for compounds that can be decomposed into a single continuous hydrophobic and hydrophilic moiety. Such active compounds can be energetically inserted most favorably into the membrane. These insertions destabilize the membrane by disrupting the internal membrane hydrogen bond network and by sliding between neighboring lipids, thereby separating them.

GLOSSÁRIO AUDIODESCRITIVO: RECURSO DIDÁTICO INCLUSIVO NO ENSINO DE QUÍMICA

School inclusion is based on the idea that all students must attend the regular education network, with the school being responsible for adapting to receive the student, through the use of teaching methodologies, accessible teaching resources, always taking into account consideration of students' needs. This work aims to establish reflections on the inclusive potential of a multiformat glossary accessible in audio and audio description as a didactic resource, working on concepts related to atomic models and concepts of general chemistry. With regard to the research methodology, it was divided into four stages, namely: the choice of platform; study on audio description; study and choice of terms to compose the glossary; and the validation of the didactic resource, which was carried out in two stages, the first a priori and the second stage a posteriori. Some of the results that were obtained with the a priori validation process of the resource with the basic education chemistry teachers and with the advisor show the need to expand the contents to be included in the resource, in relation to the a posteriori validation, carried out by the blind people and people with low vision, it can be said that they believe that the resource is valid in the sense of making chemistry more accessible, less abstract, making it possible to actually learn this discipline. In conclusion, the research carried out has significant relevance, given that it allows students to have a space for speech and more autonomy in learning.

Spin-based Atomic Periodic Model—Beyond the Periodic Table

The present article explores the potential re-arragement of all the periodic elements in terms of the spin of their valence electron, modeling them as up-spin or down-spin pointing triangles that together result in larger triangles precluding subshells, shells and the whole electron cloud (as a spin-balanced whole). Stratification of the elements according to their ratio of principal to azimuthal quantum number creates different angular cones, that not only align the elements into a compliante alternative interpretation to Mandelung&amp;rsquo;s rule, but ultimately result in a three dimensional periodic structure (that fans out and grows radially from the nucleus), resembling the shape of a flower. The model&amp;rsquo;s energetic umbrella predicts the elements Ununennium and Unbinilium. Electrical and thermal conductivities are mapped onto the new model, suggesting the existance of an instability (peak in 2D or axis in 3D) that governs the (non-) metal nature of the elements.

A Multipole-Based Reactive Force Field for Hydrocarbons.

The computational complexity of quantum chemistry methods has prompted the development of reactive force fields, facilitating practical applications of molecular dynamics simulations for large-scale reactive systems. Current reactive force fields typically employ intricate corrections based on prior chemical knowledge, which severely impedes their further advancement. This study presents a new atomic multipole-based reactive model with bond free (OPERATOR). The force field is constructed on a simple, physically motivated model within the AMOEBA framework that closely resembles the physical representation of the chemical reaction processes. In the force field, the atomic multipoles are generated dynamically according to the atomic environments, aiming to effectively capture significant changes in the electrostatic environments during chemical reactions. Subsequently, atomic multipole-based charge penetration, polarization, and charge transfer effects are incorporated into the force field to describe the complex electrostatic interactions in the system. The force field also includes van der Waals interactions and three-body potentials. In addition, to extend these nonreactive interactions to chemical reactions, the atom distribution multipole moments are used to characterize different chemical environments. The force field has been optimized using the dataset of potential energy surfaces (PESs) of hydrocarbons derived from DFT results of millions of conformations with six degrees of freedom (DOFs). The results demonstrate that the new force field effectively replicates both the monopoles and the energies. In comparison to ReaxFF, the new force field exhibits comparable or superior performance. Furthermore, molecular dynamics simulations of n-heptane decomposition effectively reproduce the primary products and reactions observed in the experiments. Given the simplicity and physically motivated nature of the model, it is expected that the new force field will be utilized in future studies to investigate chemical reaction mechanisms involving more elements.

First principle atomistic modelling of magnetic tunnel junction (MTJ) to enhance its tunnel magneto-resistance (TMR) ratio

Abstract This paper investigates the impact of electrode materials on the Tunnel Magneto-Resistance (TMR) ratio of Magnetic Tunnel Junction (MTJ) device. Four different structures of MTJ have been simulated by using cobalt (Co), Nickel (Ni), Iron (Fe) and two alloy materials of nickel-iron (NiFe) and cobalt-iron (CoFe). These materials have been used as ferromagnetic electrodes. Mulliken population and transmission spectrum observed in both parallel and antiparallel configurations of these devices to understand the spin transport properties and Tunnel Magneto-Resistance (TMR) ratio has been estimated. The first principal study was performed based on density function theory (DFT) and Non-equilibrium Green’s Function (NEGF) computational methods using the QuantumATK simulation tool to study properties such as band structure, the density of states (DOS), Spin Transfer Torque (STT), I-V characteristics and TMR. This study explores how different electrode materials affect the Tunnel Magneto-Resistance (TMR) ratio in Magnetic Tunnel Junction (MTJ) devices. With these results, it is observed that cobalt-based MTJ devices (that is Co-MgO-Fe and CoFe-MgO-CoFe) exhibit higher TMR ratio as compared to Nickel- and Iron-based MTJ devices (that is NiFe-MgO-NiFe and Ni-MgO-Fe). As Cobalt has a high spin polarization this property makes it suitable for use in spintronics devices like MTJs, where the manipulation of electron spins is essential for data storage and information processing. These findings can be employed to improve the performance characteristics of the MTJ-based Random Access Memory (MRAM) in the field of spintronics.

Bridging length and time scales in predictive simulations of thermo-mechanical processes

Abstract This work introduces a theoretical formulation and develops numerical methods for finite element implementation of the formulation so as to extend the concurrent atomistic-continuum (CAC) method for modeling and simulation of finite-temperature materials processes. With significantly reduced degrees of freedom, the CAC simulations are shown to reproduce the results of atomically resolved molecular dynamics simulations for phonon density of states, velocity distributions, equilibrium temperature field of the underlying atomistic model, and also the density, type, and structure of dislocations formed during the kinetic processes of heteroepitaxy. This work also demonstrates the need of a mesoscale tool for simulations of heteroepitaxy, as well as the unique advantage of the CAC method in simulation of the defect formation processes during heteroepitaxy.

Dynamic deformation and fracture of brass: Experiments and dislocation-based model

In this work, we perform a comprehensive study of the dynamic deformation and fracture of brass, including Taylor tests with classical and profiled cylinders and ball throwing experiments reaching the strain rates of about (0.1−1)/μs, as well as atomistic and continuum-level numerical modeling. Molecular dynamics (MD) simulations are used to construct the equation of state (EOS) of brass and to study its fracture characteristics at shear deformation under negative pressure. An original model of fracture under combined tensile-shear loading is formulated, which takes into account both the accumulation of empty volume in the process of lattice loosening due to the lattice defect production in the course of plastic deformation and further mechanical growth of voids controlled by the dislocation plasticity. This atomic-scale model is transmitted to the macroscopic experiment-scale level and embedded into 3D dislocation plasticity model to describe the dynamic deformation and fracture of brass using the numerical scheme of smoothed particle hydrodynamics (SPH). A part of experimental data is used to find the optimal parameters of the dislocation plasticity model by means of the Bayesian global optimization method accelerated with the help of artificial-neural-network (ANN)-based emulator of the 3D model. Another part of experimental data is used to fit the fracture model parameter. The remaining experimental data, which are not used in the parameterization, are applied to verify the parameterized model. The developed physical-based model provides correct and meaningful description of the dynamic deformation and fracture of brass, while the developed formalized approach to its parameterization opens a way to wider use of this type of models in the engineering applications, including studies on dynamic performance and high-speed processing technologies.