Character angle effects on dissociated dislocation core energy in aluminum
Dislocation core energy is an important property in materials mechanics but can only be obtained from atomistic simulations.
Zhou, X. W.↗
SEARCH · Engineering Papers
Results for “Atomistic simulation”
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Spatially-varying inversion near grain boundaries in MgAl 2 O 4 spinel
Atomistic simulations reveal increased cation inversion at grain boundaries in spinel. As the grain size is reduced, the apparent level of inversion in the material will increase as the grain boundaries become an increasing fraction of the material.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Topology induced crossover between Langevin, subdiffusion, and Brownian diffusion regimes in supercooled water
Despite extensive studies of supercooled water, it remains challenging to understand its peculiar dynamic anomalous properties. In this work, we integrated full atomistic simulations of supercooled water over the temperature range of room temperature to 200 K using a quantum-mechanics-based polarizable force field with the dressed dynamics method that couples fast collision events and slow reorganization dynamics of hydrogen-bond networks. Our analysis unveils the salient multiscale features in the transient relaxation dynamics of supercooled water. A classical Langevin behavior dominates at fast timescales, while long-time relaxations unveil two different activation barriers in two temperature regions: below and above 230 K. The modulation of the entropy spectrum by temperature is elucidated in terms of a three-state model underlined by the complexity of the water dynamics associated with a topological transition of a strong hydrogen-bond network. This state-dependent network topology is quantitatively characterized by power-law exponents of inverse network connectivity from 200 to 298 K. Finally, this work provides valuable guidance for further studies on the transient relaxation dynamics of supercooled water.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
An interplay between a hydrogen atmosphere and dislocation characteristics in BCC Fe from time-averaged molecular dynamics
The interplay between hydrogen and dislocations (e.g., core and elastic energies, and dislocation–dislocation interactions) has implications on hydrogen embrittlement but is poorly understood. Continuum models of hydrogen enhanced local plasticity have not considered the effect of hydrogen on dislocation core energies. Energy minimization atomistic simulations can only resolve dislocation core energies in hydrogen-free systems because hydrogen motion is omitted so hydrogen atmosphere formation can’t occur. Additionally, previous studies focused more on face-centered-cubic than body-centered-cubic metals. Discrete dislocation dynamics studies of hydrogen–dislocation interactions assume isotropic elasticity, but the validity of this assumption isn’t understood. Here, we perform time-averaged molecular dynamics simulations to study the effect of hydrogen on dislocation energies in body-centered-cubic iron for several dislocation character angles. We see atmosphere formation and highly converged dislocation energies. We find that hydrogen reduces dislocation core energies but can increase or decrease elastic energies of isolated dislocations and dislocation–dislocation interaction energies depending on character angle. We also find that isotropic elasticity can be well fitted to dislocation energies obtained from simulations if the isotropic elastic constants are not constrained to their anisotropic counterparts. These results are relevant to ongoing efforts in understanding hydrogen embrittlement and provide a foundation for future work in this field.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
ZeoNet: 3D convolutional neural networks for predicting adsorption in nanoporous zeolites
Zeolites are one of the most widely used materials in the chemical industry due to their nanometer-sized pores that can adsorb and react upon molecules selectively. With hundreds of known framework topologies and hundreds of thousands of computationally predicted structures, the ability to rapidly predict zeolite performance allows researchers to prioritize their efforts on the most promising structures for a given application. Although the accuracy of forcefield-based atomistic simulations has advanced significantly in the past two decades, these simulations can be computationally expensive, especially for long-chain, complex molecules. Here, we present ZeoNet, a representation learning framework using convolutional neural networks (ConvNets) and 3D volumetric representations for predicting adsorption in zeolites. ZeoNet was trained on the task of predicting Henry's constants for adsorption, k H , of n-octadecane in more than 330 000 known and predicted zeolite materials. Employing a 3D grid based on the distances to solvent-accessible surfaces, a volumetric representation that can be generated efficiently, the best-performing ZeoNet achieved a correlation coefficient r 2 = 0.977 and a mean-squared error MSE = 3.8 in ln k H , which corresponds to an error of 9.3 kJ mol -1 in adsorption free energy. In comparison, a model based on hand-designed geometric features has values of r 2 = 0.783 and MSE = 35.7. ZeoNet is also relatively efficient and can process ≈8 structures per second on an Nvidia RTX 2080TI GPU, orders of magnitude faster than forcefield-based simulations. A systematic analysis was conducted to investigate how the choice of ConvNet architectures, the linear dimension (L) and spatial resolution (Δd) of the distance grids, batch size, optimizer, and learning rate impact the model performance. We found that ConvNets based on the ResNet architecture offer the best tradeoff between expressiveness and efficiency. The performance for all models reaches a plateau at L = 30–45 Å and depends less sensitively on grid resolution, with a small benefit around Δd = 0.30–0.45 Å. Finally, saliency maps were visualized to identify which regions of the materials contributed the most to model predictions. It was found, interestingly, that the predictions are driven primarily by the accessible pore volume rather than the region occupied by the framework atoms.
36 MATERIALS SCIENCE↗
Structure and mechanical properties of grain boundaries in molybdenum disulfide (MoS 2 )
Molybdenum disulfide (MoS 2 ) is a two-dimensional material widely used as a lubricant in many applications involving mechanical loading under a wide range of operating temperatures. Since many synthesis and processing techniques yield MoS 2 in its polycrystalline form, establishing grain boundary (GB) structure–property relations is key to designing MoS 2 microstructures with tailored properties. Here, we employ classical, reactive atomistic simulations to investigate the structure and mechanical behavior of a wide range of GBs in MoS 2 as a function of temperature. Using a bicrystal MoS 2 geometry, we characterize the atomic structure and calculate the energy of several low-angle GBs. Then, we simulate the tensile deformation behavior of MoS 2 bicrystals at several temperatures. Our results reveal that at temperatures above 100 K, the deformation of MoS 2 bicrystals is characterized by the nucleation of shear bands from GBs that grow, with subsequent loading, into the MoS 2 crystals. At low temperatures, the tensile deformation is characterized by the nucleation and propagation of deformation fronts, resulting in altered bond angles and bond lengths. Quantitative analysis reveals a decrease in the ultimate tensile stress and ultimate failure strain of MoS 2 bicrystals with the increase in temperature. Furthermore, our simulations of the mechanical behavior of metastable GBs reveal that the strength and ductility decrease with the increase in energy of these boundary structures. In broad terms, our work provides future avenues to employ GB engineering as a strategy to tailor the properties of MoS 2 microstructures.
Moore, Robert D. [Lehigh Univ., Bethlehem, PA (Uni↗
CP2K: An Electronic Structure and Molecular Dynamics Software Package - Quickstep: Efficient and Accurate Electronic Structure Calculations
CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular and biological systems. It is especially aimed at massively-parallel and linear-scaling electronic structure methods and state-of-the-art ab-initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post-Hartree-Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension. TDK has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 716142). VRR has been supported by the Swiss National Science Foundation in the form of Ambizione grant No. PZ00P2 174227 and RZK by the Natural Sciences and Engineering Research Council of Canada (NSERC) through Discovery Grants (RGPIN-2016-0505). GKS and CJM are supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences. UK based work was funded under the embedded CSE programme of the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk), grants eCSE03-011, eCSE06-6, eCSE08-9, eCSE13-17 and the EPSRC (EP/P022235/1) grant “Surface and Interface Toolkit for the Materials Chemistry Community". Computational resources were provided by the Swiss National Supercomputing Centre (CSCS) and Compute Canada. The generous allocation of computing time on the FPGA-based supercomputer “Noctua" at PC2 is kindly acknowledged.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Rapid 3D nanoscale coherent imaging via physics-aware deep learning
Phase retrieval, the problem of recovering lost phase information from measured intensity alone, is an inverse problem that is widely faced in various imaging modalities ranging from astronomy to nanoscale imaging. The current process of phase recovery is iterative in nature. As a result, the image formation is time consuming and computationally expensive, precluding real-time imaging. Here, we use 3D nanoscale X-ray imaging as a representative example to develop a deep learning model to address this phase retrieval problem. We introduce 3D-CDI-NN, a deep convolutional neural network and differential programing framework trained to predict 3D structure and strain, solely from input 3D X-ray coherent scattering data. Our networks are designed to be “physics-aware” in multiple aspects; in that the physics of the X-ray scattering process is explicitly enforced in the training of the network, and the training data are drawn from atomistic simulations that are representative of the physics of the material. We further refine the neural network prediction through a physics-based optimization procedure to enable maximum accuracy at lowest computational cost. 3D-CDI-NN can invert a 3D coherent diffraction pattern to real-space structure and strain hundreds of times faster than traditional iterative phase retrieval methods. Our integrated machine learning and differential programing solution to the phase retrieval problem is broadly applicable across inverse problems in other application areas.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
Machine learned interatomic potential for dispersion strengthened plasma facing components
Tungsten (W) is a material of choice for the divertor material due to its high melting temperature, thermal conductivity, and sputtering threshold. However, W has a very high brittle-to-ductile transition temperature, and at fusion reactor temperatures (≥1000 K), it may undergo recrystallization and grain growth. Dispersion-strengthening W with zirconium carbide (ZrC) can improve ductility and limit grain growth, but much of the effects of the dispersoids on microstructural evolution and thermomechanical properties at high temperatures are still unknown. We present a machine learned Spectral Neighbor Analysis Potential for W–ZrC that can now be used to study these materials. In order to construct a potential suitable for large-scale atomistic simulations at fusion reactor temperatures, it is necessary to train on ab initio data generated for a diverse set of structures, chemical environments, and temperatures. Further accuracy and stability tests of the potential were achieved using objective functions for both material properties and high temperature stability. Additionally, validation of lattice parameters, surface energies, bulk moduli, and thermal expansion is confirmed on the optimized potential. Tensile tests of W/ZrC bicrystals show that although the W(110)–ZrC(111) C-terminated bicrystal has the highest ultimate tensile strength (UTS) at room temperature, observed strength decreases with increasing temperature. At 2500 K, the terminating C layer diffuses into the W, resulting in a weaker W–Zr interface. Meanwhile, the W(110)–ZrC(111) Zr-terminated bicrystal has the highest UTS at 2500 K.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Decoupling between Shockley partials and stacking faults strengthens multiprincipal element alloys
We report that mechanical properties are fundamental to structural materials, where dislocations play a decisive role in describing their mechanical behavior. Although the high-yield stresses of multiprincipal element alloys (MPEAs) have received extensive attention in the last decade, the relation between their mechanistic origins remains elusive. Our multiscale study of density functional theory, atomistic simulations, and high-resolution microscopy shows that the excellent mechanical properties of MPEAs have diverse origins. The strengthening effects through Shockley partials and stacking faults can be decoupled in MPEAs, breaking the conventional wisdom that low stacking fault energies are coupled with wide partial dislocations. This study clarifies the mechanistic origins for the strengthening effects, laying the foundation for physics-informed predictive models for materials design.
36 MATERIALS SCIENCE↗
Effect of local chemical order on the irradiation-induced defect evolution in CrCoNi medium-entropy alloy
High- (and medium-) entropy alloys have emerged as potentially suitable structural materials for nuclear applications, particularly as they appear to show promising irradiation resistance. Recent studies have provided evidence of the presence of local chemical order (LCO) as a salient feature of these complex concentrated solid-solution alloys. However, the influence of such LCO on their irradiation response has remained uncertain thus far. Here, in this work, we combine ion irradiation experiments with large-scale atomistic simulations to reveal that the presence of chemical short-range order, developed as an early stage of LCO, slows down the formation and evolution of point defects in the equiatomic medium-entropy alloy CrCoNi during irradiation. In particular, the irradiation-induced vacancies and interstitials exhibit a smaller difference in their mobility, arising from a stronger effect of LCO in localizing interstitial diffusion. This effect promotes their recombination as the LCO serves to tune the migration energy barriers of these point defects, thereby delaying the initiation of damage. These findings imply that local chemical ordering may provide a variable in the design space to enhance the resistance of multi-principal element alloys to irradiation damage.
36 MATERIALS SCIENCE↗
Probing the self-ionization of liquid water with ab initio deep potential molecular dynamics
The chemical equilibrium between self-ionized and molecular water dictates the acid–base chemistry in aqueous solutions, yet understanding the microscopic mechanisms of water self-ionization remains experimentally and computationally challenging. Herein, Density Functional Theory (DFT)–based deep neural network (DNN) potentials are combined with enhanced sampling techniques and a global acid–base collective variable to perform extensive atomistic simulations of water self-ionization for model systems of increasing size. The explicit inclusion of long-range electrostatic interactions in the DNN potential is found to be crucial to accurately reproduce the DFT free energy profile of solvated water ion pairs in small (64 and 128 H 2 O) cells. The reversible work to separate the hydroxide and hydronium to a distance S is found to converge for simulation cells containing more than 500 H 2 O, and a distance of ∼ 8 Å is the threshold beyond which the work to further separate the two ions becomes approximately zero. The slow convergence of the potential of mean force with system size is related to a restructuring of water and an increase of the local order around the water ions. Calculation of the dissociation equilibrium constant illustrates the key role of long-range electrostatics and entropic effects in the water autoionization process.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Ab initio generalized Langevin equation
We introduce a machine learning–based approach called ab initio generalized Langevin equation (AIGLE) to model the dynamics of slow collective variables (CVs) in materials and molecules. In this scheme, the parameters are learned from atomistic simulations based on ab initio quantum mechanical models. Force field, memory kernel, and noise generator are constructed in the context of the Mori–Zwanzig formalism, under the constraint of the fluctuation–dissipation theorem. Combined with deep potential molecular dynamics and electronic density functional theory, this approach opens the way to multiscale modeling in a variety of situations. Here, we demonstrate this capability with a study of two mesoscale processes in crystalline lead titanate, namely the field-driven dynamics of a planar ferroelectric domain wall, and the dynamics of an extensive lattice of coarse-grained electric dipoles. In the first case, AIGLE extends the reach of ab initio simulations to a regime of noise-driven motions not accessible to molecular dynamics. In the second case, AIGLE deals with an extensive set of CVs by adopting a local approximation for the memory kernel and retaining only short-range noise correlations. The scheme is computationally more efficient than molecular dynamics by several orders of magnitude and mimics the microscopic dynamics at low frequencies where it reproduces accurately the dominant far-infrared absorption frequency.
97 MATHEMATICS AND COMPUTING↗
An idealized model for the crystal structure of intermetallic compounds isostructural with Mg 3 Cr 2 Al 18
Mg 3 Cr 2 Al 18 (abbreviated in this report as MCA) is the parent phase for a large class of intermetallic compounds that belong to the cubic crystal space group, $Fd\overline{3}m$. The purpose of this paper is to introduce an ideal, unrelaxed crystal structure for compounds isostructural with MCA. There are five distinct atomic sublattices in MCA compounds, which can be denoted, $A, B, C, D,$ and $E$. With this, a general description for MCA structures can be written as $A^{8a}_{1}B^{16c}_{2}C^{16d}_{2}D^{48f}_{6}E^{96g}_{12}$, where the superscripts represent the Wyckoff special equipoints associated with the various sublattices in MCA, and the subscripts indicate the contributions of each sublattice to the stoichiometry of one formula unit in an any given MCA structured compound. Sublattices D and E are where deviations from ideality occur in real, MCA-like compounds. This paper examines MCA bond lengths, nearest-neighbour polyhedral arrangements, 3-D sublattice crystal structures, 2-D atom tessellation patterns, and crystal chemical effects associated with atomic relaxations on the $D$ and $E$ sublattices. The ideal MCA crystal structure developed in this report provides an appropriate initial structure for use as input to crystal structure refinements of diffraction data for MCA-like phases being examined experimentally, or as input for computational, atomistic simulations of the structures of such compounds.
36 MATERIALS SCIENCE↗
Skyrmion transport and annihilation in funnel geometries
Using atomistic simulations, we have investigated the transport and annihilation of skyrmions interacting with a funnel array under a current applied perpendicular to the funnel axis. We find that transport without annihilation is possible at low currents, when the motion is dominated by skyrmion–skyrmion interactions and skyrmions push each other through the funnel opening. Skyrmion annihilation occurs for higher currents when skyrmions in the upper half of the sample exert pressure on skyrmions in the bottom half of the sample due to the external current. Upon interacting with the funnel wall, the skyrmions undergo a size reduction that makes it easier for them to pass through the funnel opening. We find five phases as a function of the applied current and the size of the funnel opening: (i) pinned, (ii) transport without annihilation, (iii) transport with annihilation, (iv) complete annihilation, and (v) a reentrant pinning phase that only occurs for very narrow openings. Finally, our findings provide insight into how to control skyrmion transport using funnel arrays by delineating regimes in which transport of skyrmions is possible as well as the conditions under which annihilation occurs.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Reversible to irreversible transitions for ac driven skyrmions on periodic substrates
Abstract Using atomistic simulations, we investigate the dynamical behavior of magnetic skyrmions in dimer and trimer molecular crystal arrangements, as well as bipartite lattices at 3/2 and 5/2 fillings, under ac driving over a square array of anisotropy defects. For low ac amplitudes, at all fillings reversible motion appears in which the skyrmions return to their original positions at the end of each ac drive cycle and the diffusion is zero. We also identify two distinct irreversible regimes. The first is a translating regime in which the skyrmions form channels of flow in opposing directions and translate by one substrate lattice constant per ac drive cycle. The translating state appears in the dimer and trimer arrangements, and produces pronounced peaks in the diffusivity in the direction perpendicular to the external drive. For larger ac amplitudes, we find chaotic irreversible motion in which the skyrmions can randomly exchange places with each other over time, producing long-time diffusive behavior both parallel and perpendicular to the ac driving direction.
36 MATERIALS SCIENCE↗
Impact of hydrogenation on the stability and mechanical properties of amorphous boron nitride
Abstract Interconnect materials with ultralow dielectric constant, and good thermal and mechanical properties are crucial for the further miniaturization of electronic devices. Recently, it has been demonstrated that ultrathin amorphous boron nitride (aBN) films have a very low dielectric constant, high density (above 2.1 g cm −3 ), high thermal stability, and mechanical properties. The excellent properties of aBN derive from the nature and degree of disorder, which can be controlled at fabrication, allowing tuning of the physical properties for desired applications. Here, we report an improvement in the stability and mechanical properties of aBN upon hydrogen doping. With the introduction of a Gaussian approximation potential for atomistic simulations, we investigate the changing morphology of aBN with varying H doping concentrations. We found that for 8 at% of H doping, the concentration ofsp 3 -hybridized atoms reaches to a maximum which leads to an improvement of thermal stability and mechanical properties by 20%. These results will be a guideline for experimentalists and process engineers to tune the growth conditions of aBN films for numerous applications.
Materials Science↗
Decoding ionic conductivity and reordering in cation-disordered pyrochlores
The ordered structure A 2 B 2 O 6 O’ in pyrochlores engenders twin rows of inequivalent anion sublattices each centred on alternating cations. While it is known that cation antisite disorder augments the ionic conductivity by several orders of magnitude, the local cation environment around the anions and the dynamic anion reordering during the cation disordering are not well-elucidated. Using atomistic simulations on Gd 2 Zr 2 O 7 , we first show that the anions engage in concerted hops to the neighbouring tetrahedral sites mostly along with the $\langle$1 0 0$\rangle$ direction while completely avoiding the octahedral sites. While the initially vacant 8 a sites start accommodating oxygen ions with increasing cation disorder, they show noticeable reluctance even at significant levels of disorder. We have also tracked both the distribution of available oxygen sites following random cation disorder, which is dependent only on cation disordering, and the probability of occupation of these sites. Interestingly, the available oxygen sites show a non-monotonic dependence on the number of B ions in the nearest neighbouring shell while the occupation probability of all the available oxygen sites increases monotonically. A tetrahedral oxygen site thus has a better probability of being occupied when it has a greater number of second neighbour B ions. This article is part of the Theo Murphy meeting issue 'Understanding fast-ion conduction in solid electrolytes'.
Science & Technology - Other Topics↗