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.
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.
查看更多>>摘要：The extension of interatomic potentials from elemental solids to compound ones causes a bottleneck in atomistic simulations of multi-component solids such as intermetallic compounds and solid solutions. In contrast to several extensive tools released to construct elemental potentials, such as MEAMfit and ATOMICREX, very little software has been specifically designed for multi-component solids. Herein, we extend our recently proposed software EAPOTs (Empirical interAtomic POTentials for single elemental solids) to interatomic potentials of compound solids. This new software-termed as EAPOTc or the integrated Empirical interAtomic POTential optimization platform for compound solids-provides robust multi-level objective optimization strategies with various cross potential functions and extensive combinations of multiple targets such as energy, stress, force, and elasticity. Compatibility with published elemental potentials was also implemented in EAPOTc to ensure seamless combinations of different sources of elemental potentials by using the transformation invariance rules without reliability loss for the original elemental potentials. Similar to our EAPOTs code, a high-throughput (HT) scheme was designed based on automatic communication using first-principles code (e.g., VASP) to retrieve the derived properties based on energy, stress, force, and elasticity; in addition, multiple objective optimization procedures were included. The efficiency and flexibility of EAPOTc were critically validated and tested for various metallic and covalent compound systems, including HT implementation and applicability testing for extreme scenarios. Our software demonstrated several advantages, such as a concise and user-friendly graphical user interface, extensive compatibility between elemental potentials, robust optimization schemes, and a high degree of functional integration.
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：Spin-lattice dynamics is used to study the magnetic properties of Fe foams. The temperature dependence of the magnetization in foams is determined as a function of the fraction of surface atoms in foams, nsurf. The Curie temperature of foams decreases approximately linearly with nsurf, while the critical exponent of the magnetization increases considerably more strongly. If the data are plotted as a function of the fraction of surface atoms, reasonable agreement with recent data on vacancy-filled Fe crystals and novel data on void filled crystals is observed for the critical temperature. Critical temperature and critical exponent also depend on the coordination of surface atoms. Although the decrease we find is relatively small, it hints to the possibility of improved usage of topology to taylor magnetic properties.
查看更多>>摘要：Shape memory alloys (SMAs) are desirable candidates for elastocaloric effect materials, but they all suffer from large thermal hysteresis (T-hys). This study analyzes multicomponent TiNi-based SMAs dataset by machine learning (ML) to explore new SMAs with narrow T-hys. The second-largest eigenvalue lambda(2) of the stretch trans-formation matrix U is added to the original dataset to guide the ML process as a feature. Firstly, lambda(2) is obtained by first-principles calculations combined with ML. XGBoost Regressor (XGBR) combined with Leave-One-Out Cross -Validation (LOO-CV) is selected from four algorithms for modeling with the highest coefficient of determination R-2 of 0.87. The introduction of lambda(2) improves the performance of the model. The dataset is divided into 15 groups based on different doping elements (such as Hf, Cu, Zr, etc.), among which TiNiCu is the most predictive component with the R-2 of 0.89. Over 500 TiNiCu components are randomly generated and predicted T-hys. Based on the contour maps created from the prediction results, it is found that T-hys is likely to decrease with the in-crease of Cu doping in general, and minimum T-hys occurs when the Cu is about 15 at. %, which is consistent with the existing experimental results. Eventually, a potential Thys minimum (1.2 K) region of TixNiyCuz (58.3%<= x <= 58.5%, 26.5% <= y <= 27%, 14.8% <= z <= 15.3%, x +y +z =100%) SMA composition is predicted. Our study not only provides a potential selection of narrow T-hys TiNi-based SMAs but also indicates combining of XGBoost and DFT calculation is an effective strategy for materials design.
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：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.
NSTL
Elsevier