查看更多>>摘要：In Ce-Ni melts, the atomic self-diffusion coefficient decreasing nonlinearly as the Ce content decrease is observed via the ab initio molecular dynamics simulation. A mutation appears in the self-diffusion coefficient curve when the Ce content is < 33% (=Ce1Ni2). By analysing the topologic and electronic structures, this dynamic mutation is caused by the delocalization of Ce-4f electron. For Ce atom with high localized degree, the electron exchange with Ni atoms is soft. It is easy to form unstable polyhedra with coordination number between 13 and 14, which leads to faster dynamic behavior. On the contrary, Ce atom with low localized degree has slower dynamic behavior. These two types of Ce atoms vary in volume greatly and affect the atomic stacking effect. In Ce-rich and Ni-rich melts, the self-diffusion coefficient changes visibly due to the change of stacking effect. The localization degree of 4f-electron depending on composition changes are rarely observed in binary melts. This provides a good model for the further study of the electronic structures, magnetic and dynamic behavior of Ce-based melts.
查看更多>>摘要：Using density functional theory (DFT) based electronic structure calculations, we have investigated the structural, magnetic and electronic properties of CoMnSb superstructure, to elucidate its physical properties and judge its suitability for device applications. We find that the fully optimized CoMnSb superstructure exhibits half metallicity in the bulk ordered structure with a half metallic gap of width ~0.22 eV and a total spin magnetic moment of 3.75 mu B/f.u. (120 mu B/unit cell). Furthermore, we have studied the role of intrinsic defects (anti-sites and atomic swaps) and strains (uniform and bi-axial) on the half metallic property of the superstructure. We find that the crystal structure of CoMnSb superstructure is stable against intrinsic defects and half metallicity is preserved for a few type of Co(Mn) atomic swap and anti-site defects. It is also observed that, half metallicity is retained for compressive uniform strain up to-3.0% and bi-axial strain up to-3.2%. As the width of the half metallic gap increases for isotropic strain and decreases for bi-axial strain, the former is a better choice for applications. We find that in a fully strained epitaxial thin film of CoMnSb superstructure deposited on GaAs (111) surface, half metallicity is expected to be retained, with the E(f )lying within the gap. This condition is technologically more suitable compared to the unstrained condition, where the E(f )lies at the top of the valence band.
查看更多>>摘要：A phase field theory based model considering non-uniform demagnetizing field within ferromagnetic shape memory alloy and free space is established and realized by finite element method in a two-dimensional setting to save huge storage spaces and computational times. The martensite reorientation (contain nucleation and growth) from the stress field-favored martensite variant to magnetic field-favored martensite variant is numerically simulated. The simulation reasonably depicts the essence of magnetic-induced martensite reorientation and magnetization change for NiMnGa ferromagnetic shape memory alloy and their dependence on the loading level. Furthermore, the evolution and interaction of martensite domain structure, magnetic domain structure, magnetic potential and demagnetizing field are analyzed. And the dominant driving force generated by minimization of total Helmholtz free energy for loading level dependent martensite reorientation and magnetization change are revealed by addressing the influence of loading level. The simulation results show that the local demagnetizing field within ferromagnetic shape memory alloy and free space is the key to the local nucleation of new martensite variant. The dominant driving force of local nucleation and growth of new martensite variant are completely different: the local nucleation of new martensite variant is driven by local high magnetocrystalline anisotropy energy caused by local demagnetizing field; the local high Landau-type double well potential energy caused by local nucleation of new martensite variant has the largest impact on growth of the new martensite variant. The local high magnetocrystalline anisotropy energy caused by local nucleation and growth of new martensite variant has the largest impact on local rapid magnetization change within new martensite variant domain. When it comes to high loading level, the inhibition of martensite reorientation is attributed to the elastic strain energy, and the approximately uniform magnetization change is driven by Zeeman energy.
查看更多>>摘要：The competitive growth of two diverging grains in the directional solidification process was investigated through three-dimensional (3D) phase-field simulations. We explored the diverging grain boundary (GB) evolution and quantitatively analyzed the grain elimination in cases with different inclined angles of UO dendrites. It is found that the stochastic tertiary branching behavior resulted in a zigzag diverging GB. Previous two dimensional (2D) simulations about the competitive growth of diverging grains indicate a non-monotonic variation-that is, first increases and then decreases-of the grain elimination rate with the inclined angle of UO dendrites. The grain elimination in 3D diverging cases, however, shows a monotonic manner. As the spatial arrangement of FO dendrites relative to UO dendrites was a stagger configuration in 3D, not the face-to-face configuration in 2D, the competition of secondary arms at the GB region was not intense. Consequently, the liquid space size sandwiched by diverging grains became the leading factor influencing the grain elimination, and the grain elimination rate increased with the inclined angle of UO dendrites. Moreover, without the intense competition of secondary arms during the 3D diverging grain growth, the elimination of the UO grain was faster than those in 2D diverging grain growth and 3D non-uniplanar grain growth. These conclusions clarify the inconsistency between the previous 2D simulation research and the experimental research regarding the grain elimination in diverging competitive growth.
查看更多>>摘要：Solid Oxide Electrolysis Cell (SOEC) are in the focus of interest for many years, due to their ability to convert CO2 emissions to CO fuel, and thus, regenerate fuels and diminish environmental pollution. A perovskite structure (ABO3) electrode La0.3Sr0.7Fe1-xCrxO3-delta (LSFCr, A = La, Sr, B = Fe, Cr) with x = 0, 0.1, 0.2 and 0.3, was found to be a proper candidate for SOEC performance, and was tested experimentally for various properties under various environments. It has been showed that the optimal trade-off is present when x = 0.3 (La0.3Sr0.7Fe0.7-Cr0.3O3-delta) and the SrCrO4 phase was found when x > 0.33. It was also reported that Cr dopants can improve the structural stability of La0.3Sr0.7FeO3-delta in reducing atmospheres. The current work investigates by the density functional theory (DFT) method the effect of Cr stoichiometry on La(0.3)Sr(0.7)Fe(1-x)Cr(x)O(3-delta )perovskite on the electronic properties, mechanical properties, and surface catalytic activity of CO2 reduction. This work is based on our previous experimental and DFT+U study that revealed that surface models with two surface oxygen vacancies are necessary for describing CO2 reduction. Here the calculations showed that Cr atoms have an impact on the electronic behavior of LSFCr, since the bandgap depends on Cr concentration. According to the bulk modulus values, La0.3Sr0.7Fe1-xCrxO3-delta perovskite may be mechanically stable under applied stress. Moreover, the mechanical bulk modulus lowering, when SrCrO4 is present, may be an indication of possible lower stability and degradation. The calculations showed that more Cr atoms near the active site "changes your tune " on CO2 reduction by improving the reaction thermodynamically through dictating the reducing and oxidizing roles to the participating atoms in the catalysis. The low +3 oxidation state of Cr has an impact on reaction progress by creating more reductive environment.
查看更多>>摘要：AlCl3-LiCl molten salt is a promising candidate used in high-temperature batteries as cathode material to promote the development of renewable energy. Properties of AlCl3-LiCl molten salt are scarce, however, accurate and effective prediction from experienced molecular dynamics and ab initio dynamics remains a challenge. A fast and accurate simulation method based on ab initio datasets and deep neural networks, using machine learning technique, was adopted in this work. A deep potential model was constructed and trained to reproduce the energy and force of AlCl3-LiCl molten salt. Deep potential molecular dynamics simulations were carried out to investigate the local structure and properties using the deep potential model. Structural analysis including partial radial distribution function, coordination number distribution and angular distribution function suggests that the coordinated structure of Cl- around Al3+ is a stabilized and regular tetrahedron, these tetrahedrons form a sparse network liquid structure in mixtures mainly through corner-sharing. Meanwhile, properties like density, thermal expansion coefficient, specific heat capacity, self-diffusion coefficient and shear viscosity were discussed. Property discussion reveals that density and shear viscosity shows a negative relationship with temperature, the diffusivity of each ion species in AlCl3-LiCl molten salt mixture follows the order Li+ > Al3+ asymptotic to Cl- and the diffusivity increases with the rising temperature. This work enriches the fundamental data of property for AlCl3-LiCl molten salt and suggests an effective and accurate approach to other molten salt investigations in the future.
Choudhary, KamalYildirim, TanerSiderius, Daniel W.Kusne, A. Gilad...
8页
查看更多>>摘要：The increasing CO2 level is a critical concern and suitable materials are needed to capture such gases from the environment. While experimental and conventional computational methods are useful in finding such materials, they are usually slow and there is a need to expedite such processes. We use Atomistic Line Graph Neural Network (ALIGNN) method to predict CO(2 )adsorption in metal organic frameworks (MOF), which are known for their high functional tunability. We train ALIGNN models for hypothetical MOF (hMOF) database with 137953 MOFs with grand canonical Monte Carlo (GCMC) based CO2 adsorption isotherms. We develop high accuracy and fast models for pre-screening applications. We apply the trained model on CoREMOF database and computationally rank them for experimental synthesis. In addition to the CO2 adsorption isotherm, we also train models for electronic bandgaps, surface area, void fraction, lowest cavity diameter, and pore limiting diameter, and illustrate the strength and limitation of such graph neural network models. For a few candidate MOFs we carry out GCMC calculations to evaluate the deep-learning (DL) predictions.
查看更多>>摘要：The existing traditional models cannot accurately predict the gas permeability of spherical nanoparticle-mixed matrix membranes (SP-MMMs) due to ignoring the physical and chemical characteristics of the SP/matrix interface. In this paper, first, a new model is derived for the prediction of thermal conductivity of SP-polymer composites according to multiple scattering theory. Then, a new theoretical model for gas permeability of SPMMMs is developed based on the analogy with the derived model for the prediction of thermal conductivity. The significant feature of the new model is its ability to quantify the crucial role of SPs/matrix interface in gas permeability by introducing a new dense interfacial layer thickness (aint) parameter, which increases with increasing the strength of interfacial interactions. It is demonstrated that the value of aint is independent of the nature of gas molecules and mathematically correlated to the strength of SPs/matrix interfacial interactions. Finally, the gas permeability of SP-MMMs is accurately predicted by inserting the values of non-adjustable aint parameter, obtained from the correlation, diameter of SPs as well as the gas permeability of matrix into the new model, without using any adjustable parameter. Moreover, this technique can be utilized to determine the dense interfacial layer thickness in SP-polymer composites using gas permeation data.
查看更多>>摘要：This study addresses a machine learning (ML)-reinforced strategy to build both linear and non-linear property closures for metallic materials. A property closure is a closed space of material properties that contains all possible values of the closure variables. The material properties of metals are significantly dependent on the underlying microstructure texture. Here, the polycrystalline material is expressed by the Orientation Distribution Function (ODF) that relates to the volume densities of the crystallographic orientations. Theoretically, the property closures of volume-averaged material properties can be derived using single-crystal microstructure solutions; however, this theory is not valid for non-linear properties. Therefore, we use an ML-reinforced strategy to generate both linear and non-linear material property closures using the Linear Regression (LR) and Artificial Neural Network (ANN) method with Bayesian Regularization. The closures for material properties, such as the elastic stiffness parameters and critical buckling load, are generated for Titanium, Magnesium, and Aluminum. The outcomes of the ML surrogate models for these properties are compared to each other. The results demonstrate that the ANN model with Bayesian regularization is capable of predicting both linear and non-linear material properties with almost 100% accuracy. However, the linear regression algorithm is found to be not as accurate as of the Bayesian inference for the non-linear property even though it provides similar accuracy as ANN for the linear property. Therefore, ANN with Bayesian regularization is utilized for predicting the property closure of critical buckling load, which is a non-linear property.
查看更多>>摘要：3D printing (3DP) permits the integrated precise and rapid molding of low-temperature co-fired ceramics (LTCC). However, the relationship between microstructure and characteristics during sintering remains a challenge. The multi-nanoparticle sintering behavior of the ultra-low dielectric constant LTCC combination SiO2-B2O3 is reproduced by using molecular dynamics (MD) simulation. A trustworthy equivalent model to extract the effective dielectric constant (epsilon eff) of multiphase structures is developed based on fractal verification with the experimental picture. The simulated 3DP-formed 8 nm sample leads to an ultra-low dielectric constant of even lower than 3 due to the high through-hole ratio. Uniaxial tension testing of the sintered structures demonstrated the contribution of pre-pressing on the mechanical properties. The ultimate strength of the 6 nm sample formed by 3DP is approximately 1.71 Gpa, which is an increase of over 44% compared to the 8 nm sample (1.18 Gpa), but still below the yield point of the pre-pressed 6 nm sample (3.28 Gpa). Further combined with strain distribution revealed that the B2O3 aid particles, as the primary stress-bearing phase, have a significant impact on the mechanical properties. In addition, the thermal conductivity (TC) and linear thermal expansion coefficient (TEC) are evaluated to assess thermal stability. Detailed modeling of the SiO2/B2O3 interface exhibit a significant mismatch between the phonon spectra of the SiO2 and B2O3 layers in the vibrational range of 27-40 THz, which leads to differences in thermal conductivity between the 3DP formed and pre-pressed samples.
NSTL
Elsevier