Crystal Model Research Articles

Crystallization and crystal morphology of polymers: A multiphase-field study

In this paper, we introduce a coarse-grained model of polymer crystallization using a multiphase-field approach. The model combines a multiphase-field method, Nakamura’s kinetic equation, and the equation of heat conduction for studying microstructural evolution of crystallization under isothermal and non-isothermal conditions. The multiphase-field method provides flexibility in adding any number of phases with different properties making the model effective in studying blends or composite materials. We apply our model to systems of neat PA6 and study the impact of initial distribution of crystalline grains and cooling rate on the morphology of the system. The relative crystallinity (conversion) curves show qualitative agreement with experimental data. We also investigate the impact of including carbon fibers on the crystallization and grain morphology. We observe a more homogeneous crystal morphology around fibers. This is associated with the higher initial volume fraction of crystal grains and higher heat conductivity of the fiber (compared to the polymer matrix). Additionally, we observe that the crystalline grains at the fiber surface grow perpendicular to the surface. This indicates that the vertical growth observed in experiments is merely due to geometrical constraints imposed by the fiber surface and neighbouring crystalline regions.

Modeling complex polycrystalline alloys using a Generative Adversarial Network enabled computational platform

Creating statistically equivalent virtual microstructures (SEVM) for polycrystalline materials with complex microstructures that encompass multi-modal morphological and crystallographic distributions is a challenging enterprise. Cold spray-formed (CSF) AA7050 alloy containing coarse-grained prior particles and ultra-fine grains (UFG) and additively manufactured (AM) Ti64 alloys with alpha laths in beta substrates. The paper introduces an approach strategically integrating a Generative Adversarial Network (GAN) for multi-modal microstructures with a synthetic microstructure builder DREAM.3D for packing grains conforming to statistics in electron backscatter diffraction (EBSD) maps for generating SEVMs of CSF and AM alloy microstructures. A robust multiscale model is subsequently developed for self-consistent coupling of crystal plasticity finite element model (CPFEM) for coarse-grained crystals with an upscaled constitutive model for UFGs. Sub-volume elements are simulated for efficient computations and their responses are averaged for overall stress-strain response. The methods developed are important for image-based micromechanical modeling that is necessary for microstructure-property relations.

Crystallization modeling of two semi-crystalline polyamides during material extrusion additive manufacturing

In this work, a heat transfer model is developed for thermally-driven material extrusion additive manufacturing of semicrystalline polymers that considers the heat generated during crystallization by coupling crystallization kinetics with heat transfer. The materials used in this work are Technomelt PA 6910, a semicrystalline hot melt adhesive with sub-ambient glass transition temperature (Tg) and slow crystallization, and PA 6/66, a traditional semicrystalline polyamide with a higher Tg and fast crystallization. The coupled model shows that the released heat during crystallization depends on material selection, with Technomelt PA 6910 and PA 6/66’s temperatures increased by less than 1 °C and up to 6.3 °C, respectively, due to enthalpy of crystallization. Increasing the layer time decreases the layer temperature as well as the initial crystallinity. However, its effect on final crystallinity in Technomelt PA 6910 is negligible due to continued crystallization of the material after printing. Experimental validation shows good agreement for Technomelt PA 6910, but consistently underpredicts PA 6/66 crystallinity. Increasing modeled environmental temperature leads to better agreement with experimental results for PA 6/66, suggesting that higher temperatures may have been experienced. Shear-induced crystallization may also be contributing to crystallinity in this material. The results from this model highlight the importance of and interrelationships between material and processing parameter selection and can aid in achieving quality prints from semicrystalline thermoplastics.

Calibration and surrogate model-based sensitivity analysis of crystal plasticity finite element models

Crystal plasticity models are powerful tools for predicting the deformation behaviour of polycrystalline materials accounting for underlying grain morphology and texture. These models typically have a large number of parameters, an understanding of which is required to effectively calibrate and apply the model. This study presents a structured framework for the global sensitivity analysis of the effect of crystal plasticity parameters on model outputs. Due to the computational cost of evaluating crystal plasticity models multiple times within a finite element framework, a Gaussian process regression surrogate was constructed and used to conduct the sensitivity analysis. Influential parameters from the sensitivity analysis were carried forward for calibration using both a local Nelder-Mead and global differential evolution optimisation algorithm. The results show that the surrogate based global sensitivity analysis is able to efficiently identify influential crystal plasticity parameters and parameter combinations. Comparison of the Nelder-Mead and differential evolution algorithms demonstrated that only the differential evolution algorithm was able to reliably find the global optimum due to the presence of local minima in the calibration objective function. However, the performance of the differential evolution algorithm was dependent on the optimisation hyperparameters selected.

Interpretation of mechanical properties gradient in laser-welded joints: Experiments and grain morphology-dependent crystal plasticity modeling

The grain size and morphology of laser welds show a gradient distribution, leading to variations in mechanical properties. This study proposes a microscopic crystal plasticity constitutive (CPC) model to examine the effect of ER4047 welding wire on the mechanical properties of laser-welded aluminum alloy joints. A finite element model updating strategy was used to fully invert the morphological mechanical parameters, marking a significant advancement. This study combines mesoscale electron backscatter diffraction (EBSD) observations with macroscopic uniaxial tensile tests to obtain crystal plasticity finite element analysis and strain field data from digital image correlation. The mechanical property gradient of the welded joints was systematically analyzed from a crystal perspective. The CPC parameters were inverted for different grain sizes and morphologies. The study found that using ER4047 welding wire significantly enhanced the mechanical properties of the welded joints, especially in the welding zone. As grain size increased, the yield strength of the material decreased, while the hardening modulus increased. Additionally, the cast characteristics of columnar crystal morphology reduced both yield strength and hardening modulus.

Waterless structures in the Protein Data Bank.

The absence of solvent molecules in high-resolution protein crystal structure models deposited in the Protein Data Bank (PDB) contradicts the fact that, for proteins crystallized from aqueous media, water molecules are always expected to bind to the protein surface, as well as to some sites in the protein interior. An analysis of the contents of the PDB indicated that the expected ratio of the number of water molecules to the number of amino-acid residues exceeds 1.5 inatomic resolution structures, decreasing to 0.25 at around 2.5 Å resolution. Nevertheless, almost 800 protein crystal structures determined at a resolution of 2.5 Å or higher are found in the current release of the PDB without any water molecules, whereas some other depositions have unusually low or high occupancies of modeled solvent. Detailed analysis of these depositions revealed that the lack of solvent molecules might be an indication of problems with either the diffraction data, the refinement protocol, the deposition process or a combination of these factors. It is postulated that problems with solvent structure should be flagged by the PDB and addressed by the depositors.

U and Th zonation in apatite observed by synchrotron X-ray fluorescence tomography and implications for the (U–Th) ∕ He system

Abstract. Synchrotron X-ray fluorescence microtomography can non-destructively image the three-dimensional distribution of several trace elements in whole apatite crystals at the resolution of 1 µm3. This allows for precise determination of the physical geometry of a crystal and the quantification of the relative abundance of the radioactive parent nuclides uranium and thorium with high fidelity. We use these data to develop a more precise alpha ejection correction for (U–Th) / He thermochronology and high-resolution models of apatite crystals that are the foundation for a new generation of three-dimensional diffusion modeling. The application of synchrotron radiation to non-destructive imaging of minerals used for geochronology sheds light on causes of long-standing unresolved problems in the field that are rooted in previously unmeasurable parent nuclide zonation, especially the pervasive overdispersion of single-crystal ages.

Solvation modeling and optical properties of CdSO4-Doped L-Valine crystals

The utilization of computational solvation models for crystals proves effective in determining the amalgamation of molecular structures within the crystal medium, offering valuable insights into optimized crystal properties. This study focuses on identifying the arrangement of molecular strips within the crystal, crucial for recognizing active planes and the presence of non-linear optical (NLO) properties. To delve into the crystal's intricacies, a computational model was developed, and conformational analysis was conducted to ensure NLO property. The synthesis of the CdSO4-doped L-Valine metallo-organic complex crystal was achieved through the melt-freezing technique. The computational model was constructed to elucidate the formation of sub-planes within a confirmed orthorhombic crystal lattice. Mapping the molecular charge distribution with the dispersion of charge gradient facilitated the identification of chemical potential inhibition and its equipotential nodal domains. The estimation of electron density potential over critical points of the core carbons provided further insights. Chemical dynamics on the electron content of molecules for NLO properties were investigated by screening control parameters. The inelastic scattering ability of heteronuclear bonds for enhancing boosting potential was observed. Interactive frontier orbitals of degenerate energy states were illustrated, and the chemical potential of molecular zones was analyzed in-depth.

Dissymmetrization in eudialyte-group minerals. I. A model of ordered cation arrangement in the crystal structure of amableite-(Ce) using the P3 symmetry

The crystal structure of the recently discovered eudialyte group mineral, amabellite-(Ce) Na15[(Ce1.5Na1.5)Mn3]Mn2Zr3£Si[Si24O69(OH)3](OH)2 · H2O, found in the hyperagpaitic pegmatite of the Saint-Amable massif (Canada), has been solved by X-ray structural analysis within the space group R3. Amabellite-(Ce) is a member of the eudialyte group with the lowest calcium content and differs from other members of this group by the dominance of lanthanides in the part of the edge-sharing octahedra of the six-membered ring. The unit cell parameters of the mineral are: a = 14.1340(2), c = 30.378(1) Å, V = 5255.6(3) ų. A model of the cation distribution in the crystal structure of amabellite-(Ce) within the low-symmetry space group P3 has been proposed. The obtained 162 independent positions were refined in the isotropic-anisotropic approximation of atomic displacements using 3968 F 3σ(F), R = 4.6%. Despite the fairly close results, the transition from space group R3 to P3 allows for more detailed information on the local distribution of a number of elements over the framework positions. A comparison was made between the crystal structure models of amabellite within the symmetries R3 and P3, as well as other low-calcium eudialyte group minerals previously studied within several space groups.

Tripartite interaction representation algorithm for crystal graph neural networks

Driven by emerging research paradigms, the application of artificial intelligence models presents innovative tools for designing materials and optimizing their performance. In the field of materials science, there is a current research emphasis on exploring techniques for characterizing material structures to achieve precise descriptions. This paper proposes a crystal graph convolution neural network model that incorporates a tripartite interaction approach. The model not only incorporates atomic information, bond lengths, and bond angles but also offers a method for updating atoms and bond lengths, facilitating accurate descriptions of crystal structures by capturing implicit structural information. Focusing on predicting the formation energy of crystalline compounds, our results demonstrate improved predictive accuracy compared to existing representation algorithms. The average error of the formation energy in the random dataset, demonstrating robust generalization, is merely 0.048eV/atom, with an impressive R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^{2}$$\\end{document} value of 0.994. Additionally, this paper establishes a crystal graph neural network framework for predicting algorithm performance. By integrating automatic parallel algorithms and an automated process, we achieve a synthesis of these techniques, enhancing computational efficiency and streamlining algorithm usage.

Modified crystal plasticity constitutive model considering tensorial properties of microstructural evolution and creep life prediction model for Ni-based single crystal superalloy with film cooling hole

Accurate assessment of the creep life of film cooling hole structures is critical for long-life design and safe operation of aero engines and gas turbines. Firstly, through the high temperature creep experiment of nickel-based single crystal superalloy with film cooling hole, the microstructure evolution process under multiaxial stress state around film hole is characterized. Then, considering the directional effect of rafting structure and the influence of multiaxial stress, a fourth-order tensor is used to describe the evolution of γ phase width, and the microstructure evolution model accounting for multi-axial stress states is established. The microstructure evolution is coupled into the crystal plasticity constitutive model by Orowan stress. Meanwhile, based on continuous damage mechanics, a new multiaxial damage evolution law is established by introducing a multiaxial ductility factor into the constitutive model. The improved crystal plasticity constitutive model can effectively predict the microstructural evolution under multiaxial stress conditions. Furthermore, the combination of the modified crystal plasticity constitutive model and the critical distance method considering stress gradients is used for life prediction of film cooling hole structures. The prediction results show the effectiveness and necessity of considering the microstructure evolution in the life prediction.

Machine learning assisted search for Fe–Co–C ternary compounds with high magnetic anisotropy

We employ a machine learning (ML)-guided framework to explore rare earth free magnetic materials, specifically focusing on Fe–Co–C ternary compounds for potential use in permanent magnets. Utilizing a specifically trained crystal graph convolutional neural network model, we efficiently screen a vast space of nearly a million substitutional structures to select 620 promising structures for further investigation by first-principles calculation. We predict five low-energy metastable Fe–Co–C compounds with formation energy less than 150 meV/atom above the convex hull. These compounds exhibit high magnetization (Js > 1.0 T) and significant magnetic anisotropy (K1 > 1.0 MJ/m3), making them promising candidates for permanent magnet applications. The phonon calculations indicate these compounds are dynamically stable. Our ML-guided framework demonstrates the utility of rapidly identifying novel materials with tailored magnetic properties.

A crystal plasticity phase-field model for microstructure sensitive fatigue crack growth in a superalloy

In this work, a dislocation density-based crystal plasticity phase-field model (CP-PFM) is developed to simulate fatigue crack growth in nickel-based superalloys. Through normalization validation, the plastic dissipation work and crystallographic work are shown to be consistent with the fatigue indicator factors (FIPs), cumulative equivalent plastic strain and cumulative shear strain, and the two energies are computed as the main driving forces of the phase field. Both driving force models are able to obtain fatigue crack growth in close approximation to the experimental rate. However, the model with crystallographic work as the main driving force obtains crack growth paths that are in better agreement with electron backscattering pattern (EBSD) observations, which is attributed to its greater ability to characterize the microstructural susceptibility of fatigue crack growth. Specifically, the model is able to capture the tendency of cracks to crack along the close-packed planes and the hindering effect of grains with large misorientation angles on fatigue crack growth, which together contribute to the curved morphology of fatigue cracks. The combination of large grains or grains with small misorientation angles favors persistent slip band (PSB) formation and leads to softening of the crack tip, which results in lower fatigue crack growth rates.

Assessing strength of ferrite and martensite in five dual phase and two martensitic steels via high throughput nanoindentation to elucidate origins of strength

This paper describes the main findings from an experimental investigation into overall and local strength of four dual-phase (DP) steels classified based on their tensile strength (TS) levels from DP590, to 780, to 980, to 1180, a martensitic steel (MS) 1700, and a high-strength low-alloy (HSLA) steel in rolled and additively manufactured (AM) conditions. The DP steels and the MS steel consist of ferrite and martensite, while the HSLA steel is solely martensitic. High throughput nanoindentation mapping is employed to measure the mechanical hardness of individual phases contributing to strength of the steels. A clustering methodology for correlating measured hardness and phase maps is conceived to infer hardness per phase. With increasing fractions of martensite, the hardness values of both ferrite and martensite are found to increase with more rapid increase in the hardness of ferrite than martensite. The phases increasingly dislocate with the fraction of martensite as rationalized by crystal plasticity modeling of strength of the steels. Initial slip resistances and a set of hardening parameters associated with the slip in ferrite and martensite are established to model the flow stress behavior of the steels. In modeling the co-dependent nature of crystallographic slip in ferrite and martensite, the initial dislocation densities are inferred and correlated with the measured hardness values. The hardness of martensite in the rolled HSLA steel is comparable to the hardness of martensite in the MS steel, while martensite in the AM HSLA steel is softer owing to the tempering occurring during the AM process.

Antiurolithic activity of Zaleya pentandra (L.) C Jeffrey in ethylene glycol-induced calcium oxalate crystal rat model; A scientific validation of traditional use for kidney stone prevention

Ethnopharmacological relevanceTraditional herbal remedies have been used for treating nephrolithiasis, but the relevant scientific evidence is limited. Zaleya pentandra (L.) C. Jeffrey is traditionally used for the prevention of kidney stones in various cultures. However, its efficacy has not been scientifically studied. Aim of the studyThis study aimed to investigate the antiurolithic activity of Zaleya pentandra, and validate its traditional used for preventing kidney stones. Materials and methodsThe crude ethanolic extract of Z. pentandra (Zp.Crd) was evaluated through in vitro and in vivo studies. In vitro experiments assessed its impact on crystal count and morphology in metastable calcium oxalate solutions. In vivo studies involved diuretic and ethylene glycol-induced calcium oxalate crystal formation in male Wistar rats. ResultsZp.Crd transforms calcium oxalate crystals from harmful calcium oxalate monohydrate (COM) to calcium oxalate dihydrate (COD). In vivo, Zp.Crd exhibited dose-dependent (30–300 mg/kg) diuretic activity in rats by significantly increasing urinary sodium (Na+) and potassium (K+) excretion, similar to the standard diuretic hydrochlorothiazide (HCT). In the urolithiasis model, Zp.Crd exhibited dose-dependent antiurolithic effects by reducing kidney crystals and significantly altering lithogenic factors induced by ethylene glycol, including crystalluria, oxaluria, calcium, creatinine, and urea, in the urine and serum of treated rats. Zp.Crd also exhibited antioxidant effects, effectively combating oxidative lithogenic stress in rats. ConclusionZp.Crd has been shown to demonstrate antiurolithic activity against CaOx stones through CaOx crystal inhibition, diuretic activity, antioxidant properties, hypocalciuric effects, and hypercitrauric actions. The findings underscore Zp.Crd's potential as a viable alternative or supplemental therapy to current urolithiasis treatments, paving the door for further clinical trials and its inclusion into modern medical practices.

From process to property: multi-physics modeling of dislocation dynamics and microscale damage in metal additive manufacturing

AbstractMetal additive manufacturing (AM) has the potential to tailor the mechanical performance of materials. Due to the complex thermal history and unique microstructure, AM materials are reported to contain distinct dislocation networks with a high dislocation density, which affect the plastic deformation behavior and fracture. However, it is challenging to experimentally observe the formation of such dislocation structures. In this work, a multi-scale multi-physics crystal plasticity modeling framework that integrates the process-structure-property relationship in metal AM is developed. The temperature field obtained from thermal-fluid flow simulations of the AM process and the microstructure from the phase field model of grain growth are combined into thermo-mechanical crystal plasticity simulations to obtain grain-scale thermal stresses. These stresses are used as input to simulate the evolution of dislocation structures within individual grains. Taking AM 316L stainless steel as the material of interest, the effect of initial dislocation configuration on the slip plane and cross-slip mechanism on the dislocation structure formation are investigated. Furthermore, a phase field damage model is implemented to study the initiation of microscale damage and their relationship with dislocation structures, which is a main novelty of this work. This modeling framework provides comprehensive simulations of all aspects of metal AM and offers insights into the dislocation mechanisms and damage formation at microscale in AM materials, which could be used to guide the manipulation of the mechanical properties of AM materials.

High-Throughput Computational Study and Machine Learning Prediction of Electronic Properties in Transition Metal Dichalcogenide/Two-Dimensional Layered Halide Perovskite Heterostructures.

Heterostructures formed by transition metal dichalcogenides (TMDs) and two-dimensional layered halide perovskites (2D-LHPs) have attracted significant attention due to their unique optoelectronic properties. However, theoretical studies face challenges due to the large number of atoms and the need for lattice matching. With the discovery of more 2D-LHPs, there is an urgent need for methods to rapidly predict and screen TMDs/2D-LHPs heterostructures. This study employs first-principles calculations to perform high-throughput computations on 602 TMDs/2D-LHPs heterostructures. Results show that different combinations exhibit diverse band alignments, with MoS2 and WS2 more likely to form type-II heterostructures with 2D-LHPs. The highest photoelectric conversion efficiency of type-II structures reaches 23.26%, demonstrating potential applications in solar cells. Notably, some MoS2/2D-LHPs form type-S structures, showing promise in photocatalysis. Furthermore, we found that TMDs can significantly affect the conformation of organic molecules in 2D-LHPs, thus modulating the electronic properties of the heterostructures. To overcome computational cost limitations, we constructed a crystal graph convolutional neural network model based on the calculated data to predict the electronic properties of TMDs/2D-LHPs heterostructures. Using this model, we predicted the bandgaps and band alignment types of 9,360 TMDs/2D-LHPs heterostructures, providing a comprehensive theoretical reference for research in this field.

Predictive models for cholesterol crystals and plaque vulnerability in acute myocardial infarction: Insights from an optical coherence tomography study

BackgroundCholesterol crystals (CCs) are recognized as a risk factor for vulnerable atherosclerotic plaque rupture (PR) and major adverse cardiovascular events. However, their predictive factors and association with plaque vulnerability in patients with acute myocardial infarction (AMI) remain insufficiently explored. Therefore, This study aims to investigate the association between CCs and plaque vulnerability in culprit lesions of AMI patients, identify the factors influencing CCs formation, and develop a predictive model for CCs. MethodsA total of 431 culprit lesions from AMI patients who underwent pre-intervention optical coherence tomography (OCT) imaging were analyzed. Patients were divided into groups based on the presence or absence of CCs and PR. The relationship between CCs and plaque vulnerability was evaluated. A risk nomogram for predicting CCs was developed using the least absolute shrinkage and selection operator and logistic regression analysis. ResultsCCs were identified in 64.5 % of patients with AMI. The presence of CCs was associated with a higher prevalence of vulnerable plaque features, such as thin-cap fibroatheroma (TCFA), PR, macrophage infiltration, neovascularization, calcification, and thrombus, compared to patients without CCs. The CCs model demonstrated an area under the curve (AUC) of 0.676 for predicting PR. Incorporating CCs into the TCFA model (AUC = 0.656) significantly enhanced predictive accuracy, with a net reclassification improvement index of 0.462 (95 % confidence interval [CI]: 0.263–0.661, p < 0.001) and an integrated discrimination improvement index of 0.031 (95 % CI: 0.013–0.048, p = 0.001). Multivariate regression analysis identified the atherogenic index of plasma (odds ratio [OR] = 2.417), TCFA (OR = 1.759), macrophage infiltration (OR = 3.863), neovascularization (OR = 2.697), calcification (OR = 1.860), and thrombus (OR = 2.430) as independent risk factors for CCs formation. The comprehensive model incorporating these factors exhibited reasonable discriminatory ability, with an AUC of 0.766 (95 % CI: 0.717–0.815) in the training set and 0.753 (95 % CI: 0.704–0.802) in the internal validation set, reflecting good calibration. Decision curve analysis suggested that the model has potential clinical utility within a threshold probability range of approximately 18 % to 85 %. ConclusionsCCs were associated with plaque vulnerability in the culprit lesions of AMI patients. Additionally, this study identified key factors influencing CCs formation and developed a predictive model with potential clinical applicability.

A novel concurrent multiscale method based on the coupling of Direct FE2 and CPFEM

Performing concurrent simulations of macroscopic behaviors and microscopic structures using the crystal plasticity finite element method (CPFEM) presents a substantial difficulty with existing numerical techniques. To address this issue, a novel multi-scale method is proposed that couples CPFEM with a multiscale FEM, specifically Direct FE2. This facilitates the implementation of Direct CP-FE2 in this work. The micro representative volume elements (RVEs) equipped with a crystal plasticity constitutive model and the macro mesh are integrated into a monolithic solution scheme within the Direct FE2 framework. The proposed method integrates the multiscale simulation capability of Direct FE2 with the crystal plasticity model of CPFEM. Alpha titanium (α-Ti), which exhibits two distinct plastic mechanisms of slip and twinning, is chosen as the subject of investigation for conducting numerical experiments. The accuracy and efficiency of the Direct CP-FE2 model are evaluated through multiple plate tension and beam bending tests. The effective validation against the FEM model demonstrated the capability of Direct CP-FE2 to forecast macroscopic deformation behaviors. Meanwhile, the Direct CP-FE2 model can reveal the activation of slip/twinning systems and the evolution of crystal texture at a microscopic level. The influence of the grain orientation-dependent effect can be well considered into the macroscopic analysis with the help of Direct CP-FE2. Based on the testing examples, we demonstrate that the yield state of the macrostructure is enhanced when the crystal orientation is closer to the (0001) direction. Consequently, there exist very little crystal rotation behavior, hindering the evolution of the crystal texture.

Effect of various interlayers in NiTi to TC4 dissimilar joints by magnetic pulse welding

Magnetic pulse welding (MPW) has gained wide attention recently. In this paper, the connection of NiTi/TC4 dissimilar materials was achieved by adding pure Al, Cu, and Ni interlayers using MPW technology, and microstructure characterization and mechanical property tests were conducted to evaluate the joints. The results show that the atomic diffusion in the welding process plays a key role in the interface formation process. The interfacial morphology of joints consists of wavy and flat interfaces, and the mechanical interlocking of the wavy interface gives the joints excellent mechanical properties. The joints with the pure Al interlayer have the best mechanical properties, and the tensile shear force is 2.168 kN; the fracture forms of joints with different interlayers are all ductile fractures. In addition, the calculation results of the edge-to-edge crystal matching model show that the side with the high interface mismatch rate is where the joint failure problem occurs frequently.