Computational Materials Science最新文献

Due to its special porous structure with smooth continuous surface and high specific surface area, the triply periodic minimal surface (TPMS) lattice structure exhibits excellent properties such as strong bearing capacity, high energy absorption rate and good fatigue performance. The residual stresses generated during the additive manufacturing (AM) process can have a significant impact on the mechanical properties of the TPMS structure. In this paper, the AM process of four typical TPMS structures are investigated by the thermal–mechanical coupling model. The mechanism of residual stress generation is analyzed, and an optimized preparation process scheme is proposed to reduce the residual stress. Furthermore, the effects of residual stresses on the mechanical properties of TPMS structures are investigated for different types, volume fractions and compression directions. Results show that the influences of scanning speed and hatch spacing on the residual stress are not significant with constant laser power, but the deposition thickness should be adjusted according to the characteristics of the structure. The residual stress will reduce the elastic modulus and yield strength, while no obvious effect on the plastic behavior is observed. Importantly, the residual stress has the greatest influence on the mechanical properties of I-WP-type among the four investigated types, which becomes more pronounced with the increase of volume fraction. Moreover, the influence of residual stress on the mechanical properties of TPMS structures depends on the compression direction. Our results give a comprehensive understanding of the residual stress distribution and impact on the mechanical properties of TPMS structures, providing guidance to the rational design and optimization of TPMS structures in engineering applications.

The adsorption of intact and dissociated water molecules on the surfaces of the Pt₃Zr alloy and pure Zr have been investigated by means of density functional theory simulations. In each case, a varying amount of water molecules was placed on the surface until saturation coverage was reached. For both surfaces, all the energy barriers for the partial and complete decomposition of water were calculated. The partial dissociation of H₂O into OH and H, and the complete dissociation into O and two H atoms are significantly more difficult on Pt₃Zr surfaces, as compared to pure Zr surfaces: the dissociative adsorption energies are smaller and the activation barriers for dissociation are larger in Pt₃Zr. In addition, the recombination of H atoms into H₂ molecules and desorption of those molecules is easier on the Pt₃Zr surfaces. The results suggest that the use of the Pt₃Zr alloy as a protective coating in Zr-based metallic components used in nuclear reactors can indeed improve their performance, since the alloyed Pt₃Zr layers are much more resistant towards oxidation and H attack than pure Zr in the presence of hot water vapor.

This paper investigates Pickering emulsions (PEs), formed by suspending oil droplets as the dispersed phase in water as the continuous phase using uncharged particles at their interface. To prevent coalescence, the particles are grafted with polymer chains, as emulsifier, not only to provide steric repulsion forces through the excluded volume between monomers floating in water, compensating for the absence of Coulombic repulsive forces but also control emulsion stabilization/destabilization. We combined molecular dynamics (MD) simulations and a generalized Langevin equation (GLE) model to study the dynamics of hairy-droplets using two dynamic quantities: mean square displacement (MSD) and velocity autocorrelation function (VACF). The number of grafted-polymer-chains, $f$, was the main parameter of interest, while the other parameters of the system were kept constant. A statistical approach was used to estimate theoretical GLE-based model parameters along with their uncertainties, providing insight into the diffusion behavior of these hairy-droplets and specifically addressing the transition between different observed regimes. we capture three specific values of grafted-polymer-chains as, $f_b=47$ and $f_t=185$ describe the transitions of mushroom-brush conformation of grafted-polymer-chains and viscous-viscoelastic behavior of hairy-droplets, respectively, and $f_g=257$ representing the gel-like state number.

The purpose of this study is to investigate the adsorption mechanism of hydroxyl polytridecaaluminum (Al₁₃) on kaolinite (0 0 1) and (00 $\overline{\text{1}}$) surfaces during the flocculation of polyaluminum chloride. Active sites of hydroxyl polytridecaaluminum and adsorption sites on the kaolinite surface were determined by frontier orbital and Mulliken charge, and different adsorption models were constructed. Density functional theory was used to calculate these models. The results demonstrate that the adsorption of Al₁₃ on kaolinite (0 0 1) and (00 $\overline{\text{1}}$) surfaces all occurs spontaneously, and the adsorption models with the lowest energy on (0 0 1) and (00 $\overline{\text{1}}$) surfaces are formed by five and four hydrogen bonds, respectively. Mulliken charge distribution analysis shows that significant electron transfer occurs in both models, with transfer amounts of 1.16 e and 0.54 e, respectively. The analysis of the partial density of states shows that two Al atoms in the two Al–O octahedra in the outer layer of Al₁₃ have certain bonding interactions with the O atoms of the kaolinite surfaces. The calculations indicate that the interactions of Al₁₃ with kaolinite (0 0 1) and (00 $\overline{\text{1}}$) surfaces are mainly electrostatic interactions and hydrogen bonding, and the adsorption strength of Al₁₃ on the (0 0 1) surface is higher than that on the (00 $\overline{\text{1}}$) surface.

In this paper, we introduce a Bayesian framework designed for inverse inference, aiming to predict material properties/process parameters from microstructure images. The integration of Bayesian inference techniques with deep generative models establishes a robust tool for applications in materials science, particularly in material characterization and property control. This integration provides a novel approach to clarifying the reliability of predictions. The application of this framework to a sample problem involving the prediction of material properties from artificial dual-phase steel microstructures demonstrates its capability to estimate these properties while accounting for prediction uncertainties. Moreover, even in comparison to conventional regression methods in terms of point estimation, the proposed framework exhibits superior accuracy in prediction. These results clearly illustrate that the framework presented in this paper constitutes a powerful tool for achieving efficient material design.

The structural, thermodynamic and transport properties of the CaF₂-MgF₂ molten salt system were investigated with ab initio molecular dynamics (AIMD), system-specific neural network interatomic potentials (NNIPs) and universal PreFerred Potentials (PFP). We trained an NNIP model using AIMD data as input and used this potential to efficiently simulate the interactions within a large supercell in a temperature range of 1273–1773 K. The Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code was employed to validate our trained NNIP model. The Matlantis software with universal PFP is also presented to prove its feasibility for MD calculations and can be considered as a useful alternative simulation tool for higher-order systems where existing potentials are not readily available. We calculated structural and thermodynamic properties including radial distribution function (RDF), angular distribution function (ADF), specific heat capacity, ionic self-diffusivity, and viscosity. Our results indicate that the system exhibited a high degree of structural disorder, with the Ca, Mg, and F ions forming a liquid solution. Using PFP, the positions of the first peak in RDFs for Ca-F and Mg-F pairs are only slightly left-shifted (<0.05 Å), and the estimated viscosity of the melt decreases from 4.613 mPa·s to 1.846 mPa·s with an increase in temperature from 1273 K to 1773 K, in agreement with the NNIP trained specifically for CaF₂-MgF₂. Our results provide valuable insights into the properties of the CaF₂-MgF₂ system at high temperatures and serve as predictive models for the development of new electrolytes that could be used for silicon epitaxy by adding silica.

A GPU-accelerated cellular automata-lattice Boltzmann combinatorial model is developed for calculating the preferred growth, movement, and collision behavior of equiaxed crystals in supercooled melts of binary alloys. For moving dendrites, the growth is computed in a dynamic grid that grows with the body, and continuous movement is achieved by moving the dynamic grid. The impulse-based method is used for the collision of dendrites to calculate the post-collision velocity. Each module of the model was rigorously benchmarked, proving that the model has good computational accuracy and efficiency. The model was used for modeling the solidification of an Al-3 wt% Cu alloy, simulating the growth of abundant kinematic equiaxed crystals in a rotating flow and the falling and stacking of dendrites in droves and subsequent grain growth during the columnar to equiaxed transition, respectively.

The dissolution of stoichiometric particles within a melt plays a crucial role in various material processes. This study presents a comprehensive phase-field model to analyze the dissolution behavior of these stoichiometric particles under experimental conditions. Our approach addresses the classical phase-field challenges related to modeling stoichiometric compounds and scaling to experimentally relevant lengths in a multi-phase, multi-component context. To overcome the difficulties posed by stoichiometric compounds, we rederive the classical phase-field evolution equations for a multi-phase system, adopting a composition-independent free energy expression for the stoichiometric compound. Additionally, we extend Feyen’s high driving force model [Feyen and Moelans, Acta Materialia, 256 (2023)] to multi-component systems, allowing us to perform quantitative simulations for technologically relevant material systems at experimental length scales within a reasonable computing time. The model’s precision in capturing diffusion-controlled transformations, including dissolution, growth, and the Gibbs–Thomson effect, is validated against analytical solutions for a hypothetical system. The quantitative nature of the model is validated by applying it to the dissolution of Al₂O₃ particles in CaO–Al₂O₃–SiO₂ slags. We break new ground by conducting three-dimensional simulations for a system size of $875\mu m\times 875\mu m\times 875\mu m$, directly comparable to confocal scanning laser microscopy experiments, where previous models were limited to two-dimensional simulations and a system size of $2\mu m\times 2\mu m$. This validation underscores the model’s proficiency to quantitatively describe the diffusion-controlled dissolution of Al₂O₃ at the experimentally relevant length scales.

This work aims to investigate complex relationship between microstructure characteristics and mechanical properties of dual phase (DP) steel through an inverse analysis based on Markov chain Monte Carlo (MCMC) method combined with meso-scale material modelling. In this framework, a machine learning approach as surrogate model was developed, in which support vector regression (SVR) and artificial neural network (ANN) were trained using results from representative volume element (RVE) simulations coupled with damage model, which were previously calibrated with experimental data of commercial DP steel grades. Moreover, specific microstructure descriptors including Moran’s index, martensite band index and martensite orientation were proposed for representing effects of spatial distributions of martensitic phase. As a result, inverse predictions of microstructure characteristics of DP steels for achieving defined yield strength, tensile strength, uniform elongation and toughness were presented. The inverse analysis could solve the non-uniqueness of structure–property relationships of steel, whereby significances of dispersed structures and aligned martensite bands were highlighted in details. The approach fairly dealt with multi-target optimization and high dimensional problem, which can be further applied as a guideline for designing DP microstructures with enhanced mechanical properties.

Dynamic structural response of nano-polycrystalline graphite under shock compression is investigated using molecular dynamics (MD) simulations. Hugoniot data shows that the structural transition is activated at shock pressure P∼30 GPa (experimental range, 20–50 GPa), resulting in the formation and extension of hexagonal diamond nuclei along grain boundaries, embedded incoherently among thin-graphite grains. As P increases from 130 GPa, the structure starts to liquefy, accompanied by a decrease in shear stress τ from approximately 5.3 GPa, and completely liquefies at P∼250 GPa (melting pressure of graphite, 180–280 GPa) and τ ∼ 0 GPa. In ultrahigh-pressure region, a two-wave structure is generated consisting of an elastic shock wave and a phase transition wave, and when the piston velocity exceeds 5.2 km/s, the latter wave can catch up with the elastic one, eventually becoming a single over-driven wave. During the relaxation of compressed nano-polycrystalline graphite, void nucleation inside the sample induces the initiation of visible cracks when piston velocity is higher than 1 km/s. At low piston velocities, the cracks propagate gradually along grain boundaries due to shear-slip effects. While at high piston velocities, direct spall of the nano-polycrystalline graphite makes it into multiple fragments by ultrahigh strain rate tensile forces. This study provides a useful guide to the structural transition and dynamic damage evolution of nano-polycrystalline graphite under shock compression.