查看更多>>摘要：With the development of the density functional theory (DFT) and ever-increasing computational capacity， an accurate prediction of lattice thermal conductivity based on the Boltzmann transport theory becomes computationally feasible， contributing to a fundamental understanding of thermal conductivity as well as a choice of the optimal materials for specific applications。 However， steep costs in evaluating third-order force constants limit the theoretical investigation to crystals with high symmetry and few atoms in the unit cell。 Currently， machine learning potentials are garnering attention as a computationally efficient high-fidelity model of DFT， and several studies have demonstrated that the lattice thermal conductivity could be computed accurately via the machine learning potentials。 However， test materials were mostly crystals with high symmetries， and the applicability of machine learning potentials to a wide range of materials has yet to be demonstrated。 Furthermore， establishing a standard training set that provides consistent accuracy and computational efficiencies across a wide range of materials would be useful。 To address these issues， herein we compute lattice thermal conductivities at 300 K using neural network interatomic potentials。 As test materials， we select 25 materials with diverse symmetries and a wide range of lattice thermal conductivities between 10-1 and 10(3) Wm(-1)K(-1)。 Among various choices of training sets， we find that molecular dynamics trajectories at 50-700 K consistently provide results at par with DFT for the test materials。 In contrast to pure DFT approaches， the computational cost in the present approach is uniform over the test materials， yielding a speed gain of 2-10 folds。 When a smaller reduced training set is used， the relative efficiency increases by up to ~50 folds without sacrificing accuracy significantly。 The current work will broaden the application scope of machine learning potentials by establishing a robust framework for accurately computing lattice thermal conductivity with machine learning potentials。
查看更多>>摘要：In this work， molecular dynamics (MD) simulations were used to investigate elementary dislocation properties in a Co-free high entropy (HEA) model alloy (Cr15Fe46Mn17Ni22 at。 %) in comparison with a model alloy representative of Austenitic Stainless Steel (ASS) (Cr20Fe70Ni10 at。 %)。 Recently developed embedded-atom method (EAM) potentials were used to describe the atomic interactions in the alloys。 Molecular Statics (MS) calculations were used to study the dislocation properties in terms of local stacking fault energy (SFE)， dissociation distance while MD was used to investigate the dissociation distance under applied shear stress as a function of temperature and strain rate。 It was shown that higher critical stress is required to move dislocations in the HEA alloy compared with the ASS model alloy。 The theoretical investigation of simulation results of the dislocation mobility shows that a simple constitutive mobility law allows to predict dislocation velocity in both alloys over three orders of magnitude， covering the phonon drag regime and the thermally activated regime induced by dislocation unpinning from local hard configurations。
查看更多>>摘要：CoCrNi multicomponent alloys with equiatomic and non-equiatomic concentrations are promising for achieving an excellent balance of strength and ductility。 However， the correlations between elemental concentrations and deformation mechanisms of this alloy family need to be uncovered for further targeted alloy design。 In this work， atomistic molecular dynamics simulations were performed to study the effects of Co concentration on the mechanical behaviors and underlying deformation mechanisms of single-crystal Co-x(CrNi)(100-x) (x = 13， 23， 33。3， 50 at。 %) alloys。 Atomic-scale microstructural evolution with increasing the compressive strain was revealed in each specimen with different Co concentrations。 The results suggest that the yield stress and elastic modulus are enhanced with increasing the Co content。 Atomic pairs containing Cr exhibit the lowest cohesion energy among all pair types， and therefore Cr atoms participate most actively in dislocation nucleation。 Although dislocation slip comes into play in all specimens， the primary deformation mechanism changes from the multilayering of stacking faults (SFs) to the formation of primary-secondary twin pairs with the increase of Co content。 Further， the twinnability of these alloys was estimated using a theoretical model based on the generalized stacking fault energy (GSFE) curves。 The results indicate that the Co50Cr25Ni25 alloy owns the highest twinning tendency among the probed alloy variants， which promotes the formation of secondary deformation twins and leads to high deformability。 This work provides guidelines for the design of non-equiatomic CoCrNi multicomponent alloys with proper Co content for desirable mechanical properties。
查看更多>>摘要：This current ReaxFF simulation provides atomic-level monitoring of the reaction details as well as dynamics of Albite under acidic solution over a nanosecond time scale。 30% H-O bond dissociation in 10% H2SO4 solution and similar to 0。9% Si-O/Al-O bonds breakage in Albite during 20 ps was evaluated according to the time evolution of number of H-O bonds， Si-O and Al-O bonds at a higher temperature of 323 K。 The breakage of Si-O/Al-O bonds within the tetrahedron configuration makes Na atoms be less restricted within the network and generates open channel for Na migration， resulting in further diffusion to the Albite-water interface and complete release into the aqueous medium。 A consecutive dissociation of H2SO4 can further accelerate the formation of SO42- products as the H dissociate from H2SO4 molecules。 The migrated Na cations tend to combine with SO42- and form Na-SO4 ions pair， leading to a complicated zigzag motion in 3D space and a migration trend towards the aqueous me-dium along Z-axis direction and a sharp peak at 2。3 angstrom in Na-O pair RDF。
查看更多>>摘要：Carbon allotropes can be considered as an excellent radiation resistance enhancer in metal-Graphene nano-composites (NCs)。 Many research groups studied the radiation resistance and interface stability of metal-Graphene NCs and revealed that Graphene can improve the radiation resistance of NCs under irradiation。 Other allotropes of carbon have not been studied up to now。 Therefore， in this work， four Cu-based NCs such as Cu-Graphene， Cu-Graphyne， Cu-Graphdiyne， and Cu-Graphane were studied by molecular dynamics (MD) to understand the role of carbon allotropes on the radiation resistance of Cu-based NCs。 Compared with pure copper; four Cu-based NCs have fewer residual defects in the bulk region after irradiation。 The results demon-strated that the interface of these NCs acts as a sink for radiation-induced defects， and preferentially traps in-terstitials over vacancies。 The results of energetic calculations indicate the defect formation energy is reduced in the vicinity of interface regions。 Compared with Cu-Graphyne and Cu-Graphdiyne， Cu-Graphene and Cu-Graphane have low segregation energy for interstitial emission mechanism to annihilate vacancies。 Also， Cu/ Graphane/Cu interface has a higher strength of interaction and attraction with vacancies due to the low value of vacancy formation energy (E-vac)。 Thermodynamic and structural analyses reveal that Graphdiyne plane isn't a stable plane during collision cascades。 The results of this study can provide a fundamental perspective on the radiation resistance of Cu-based NCs including different carbon allotropes to select the best allotrope to improve the radiation resistance of NC for using in extreme radiation environments。
查看更多>>摘要：Deep learning-based object detection models have recently found widespread use in materials science， with rapid progress made in just the past two years。 Scanning and tunneling electron microscopy methods are among the most important and widely used characterization techniques for understanding fundamental materials structure-property-performance linkages from the micron to atomic scale。 Dramatic increases in dataset size and complexity from modern electron microscopy instruments have necessitated the development and use of automated methods of extracting pertinent features of images。 Here， the use of object detection in materials science， with a focus on the analysis of features in electron microscopy images， is reviewed。 Key findings and limitations of recent seminal studies using object detection to characterize and quantify defects in irradiated metal alloys， segment and analyze micro and nanoparticles， find individual atoms at the nanoscale， and detect and track objects from in situ video are reviewed。 Opportunities and challenges presently facing the materials community are highlighted， where discussion of best practices for model assessment and applicability are presented， along with the potential of improved model training with synthetic data。 This review concludes with offering more speculative， forward-looking thoughts on the potential of the broader materials community to construct a living ecosystem integrating community-consensus curated data and validated models as tools to best inform application of object detection and segmentation models to specific materials domains。
Gradwohl, Kevin-P.Miller, WolframDropka, NatashaSumathi, R. Radhakrishnan...
7页
查看更多>>摘要：This is the first report of a quantitative dislocation multiplication law for Ge single crystals based on discrete dislocation dynamics simulations。 The multiplication was studied as a function of dislocation density and effective shear stress in periodic boundary conditions close to melting point of Ge， utilizing a specifically developed diamond cubic dislocation mobility module in agreement with experiments in literature。 We report an average dislocation velocity law of a dislocation ensemble linearly proportional to the resolved shear stress - analogous to the single dislocation velocity law - with a reduced average dislocation mobility。 Exponential dislocation multiplication was observed with a multiplication parameter - linear proportional to the effective shear stress for various stress states and simulation volumes。 The coefficient of the dislocation multiplication law was determined to be 4。0 。10-3 [mN(-1)]。
查看更多>>摘要：The oscillation of a graphene flake on a substrate with undulated surface is investigated by classical molecular dynamics simulation。 The gradient in amplitude of the undulation is found to provide the driving force for the motion of the graphene flake， which slides on top of a graphene layer that well conforms to the substrate。 The oscillatory motion of the flake can be well described by the equation of motion of a damped oscillator， with damping factor corresponding to the friction coefficient between the flake and the graphene layer on which it glides。 When the amplitude gradient increases， the oscillation frequency increases as well。 The shape of the graphene flake is found to have a strong influence on friction， as some geometries promote in-plane rotation。 The results in the present study point to an alternative approach to transport or manipulation of nanosized objects。
查看更多>>摘要：Machine learning (ML) is widely used in the field of material informatics。 However， limitations on the size of available datasets are a key bottleneck in the use of machine learning methods to predict material properties or reverse-design high-performance materials。 To solve this problem， we propose a virtual sample generation algorithm based on a Gaussian mixture model (GMM-VSG) to address the lack of training samples in machine learning。 The core idea of the algorithm is to generate virtual samples by fitting the distribution of the original samples。 We used an open rubber composite dataset (24 samples) to establish a machine learning model to predict the wear resistance of rubber materials through mechanical properties to verify the performance of the GMM-VSG algorithm。 The results show that after using our algorithm， the R2 of the prediction model reached 0。95， and the prediction accuracy increased by 41%。 This shows that the proposed algorithm can effectively promote the prediction accuracy of data model with small sample size。
查看更多>>摘要：We energetically predicted the morphology of Zr hydride precipitates in a hexagonal close-packed (HCP) Zr matrix。 Considering Zr hydride precipitates as ellipsoids， we used Eshelby's ellipsoidal inclusions to calculate the elastic energy increment due to the presence of Zr hydride precipitates in the Zr matrix， in which the elastic anisotropy and inhomogeneity of the elastic constants between Zr and Zr hydride were considered。 We compared the difference in the elastic energy increment between the ellipsoidal inclusions with different shapes: plates (mimicked by penny-shape ellipsoids)， needles (mimicked by longitudinal ellipsoids) and sphere， and orientations to detect the stable structure with the minimum elastic energy increment。 Eigenstrains of each Zr hydride and elastic constants of Zr hydrides and HCP Zr for Eshelby's ellipsoidal inclusion analysis were determined using atomistic simulations based on a density functional theory calculation， achieving a parameter free ab initio morphology prediction。 The morphology predictions were implemented for two cases: with and without shear components of eigenstrain (w/ and w/o shear)。 The (1210) longitudinal needle for the y hydride (w/o shear) and plate (or disk) on the plane， which is 20? to 30? tilted about (1210)-axis from basal plane (0001)， for delta and e hydrides (w/ shear) were successfully predicted as stable shapes and orientations of the precipitates under zero external stress conditions， qualitatively consistent with experimental observations。 The external circumferential tensile stress on the basal plane reduces the elastic energy of [0001] parallel Zr hydride plates， which is also qualitatively consistent with the reoriented delta hydride precipitates observed in the experiment。 On the other hand， predicted external stress for the reorientation of Zr hydride is quite high， around 10 GPa。 This is inconsistent with experimental observation and further investigation is necessary。 Generally， our predictions based on elasticity theory appear qualitatively consistent with experimental observations， suggesting an elastic origin of the morphology of Zr hydride precipitates in the HCP Zr matrix。
NSTL
Elsevier