Computational Materials Science最新文献

Linking atomistic information on solute interactions with microstructure evolution is a key challenge for predictive modelling of chemistry effects on material properties. The objective of the present work is to provide a link between grain boundary segregation energies with GB migration kinetics on the example of recrystallization in multi-component ferritic steels. For that purpose, the segregation of 64 elements from the periodic table to a representative grain boundary is computed with ab initio density functional theory. To connect this data to grain boundary migration kinetics, the solute trend parameter from a simplified solute drag treatment is employed. The solute trend parameter for individual solutes is presented, which highlights solutes with large impact on grain boundary migration. Furthermore, an extension of the solute trend parameter is introduced that allows to evaluate the solute drag potential of realistic steel compositions. The necessity to include solute co-segregation, site competition, and precipitation effects is shown in a comparison to experimental data on recrystallization kinetics. The comparison to experimental data demonstrates the qualitative predictability of recrystallization kinetics by the extended solute trend parameter.

The microstructure of metals and foams can be effectively modelled with anisotropic power diagrams (APDs), which provide control over the shape of individual grains. One major obstacle to the wider adoption of APDs is the computational cost that is associated with their generation. We propose a novel approach to generate APDs with prescribed statistical properties, including fine control over the size of individual grains. To this end, we rely on fast optimal transport algorithms that stream well on Graphics Processing Units (GPU) and handle non-uniform, anisotropic distance functions. This allows us to find large APDs that best fit experimental data and generate synthetic high-resolution microstructures in (tens of) seconds. This unlocks their use for computational homogenisation, which is especially relevant to machine learning methods that require the generation of large collections of representative microstructures as training data. The paper is accompanied by a Python library, PyAPD, which is freely available at: www.github.com/mbuze/PyAPD.

We developed a method for fitting machine-learning interatomic potentials with magnetic degrees of freedom, namely, magnetic Moment Tensor Potentials (mMTP). The main feature of our method consists in fitting mMTP to magnetic forces (negative derivatives of energies with respect to magnetic moments) as obtained spin-polarized density functional theory calculations. We test our method on the bcc Fe–Al system with different compositions. Specifically, we calculate formation energies, equilibrium lattice parameter, and total cell magnetization. Our findings demonstrate an accurate correspondence between the values calculated with mMTP and those obtained by DFT at zero temperature. Additionally, using molecular dynamics, we estimate the finite-temperature lattice parameter and capture the cell expansion as was previously revealed in experiment. Furthermore, we demonstrate that fitting to magnetic forces increases the reliability of structure relaxation (or, equilibration), in the sense of ensuring that every relaxation run ends up with a successfully relaxed structure (the failure may otherwise be caused by falsely driving a configuration away from the region covered in the training set).

To further improve the applicability of perovskite materials in photovoltaics, exploring perovskites with appropriate band gaps and enhanced stability is essential. Nevertheless, identifying promising perovskite materials through a perennial trial-and-error approach is both time-consuming and expensive. In this study, we introduce a method that combines machine learning (ML) and density functional theory (DFT) calculations to efficiently screen inorganic perovskite materials for photovoltaic applications. By utilizing 107 experimental data, we built a machine learning regression model capable of predicting the maximum power conversion efficiency (PCE) achieved in experiments. Light Gradient Boosting Machine (Lightgbm) exhibited superior performance with a test set R² score of 0.89. Simultaneously, another machine learning regression model was trained using 405 data to predict the theoretical maximum PCE. The best-performing model was Extreme Gradient Boosting (Xgboost) with a test set R² score of 0.93. By integrating these ML models with DFT calculations, we identified three potential inorganic perovskites: CsPdCl₃, KGeCl₃, and CsCu₂Br₃. These materials exhibit direct bandgaps of 1.47 eV, 1.37 eV, and 1.65 eV respectively, along with high thermal stability and favorable optical properties. This method constructs an experimental-theoretical-data driven framework for the prediction of inorganic perovskites, effectively reducing the research cycle in perovskite photovoltaics.

Understanding irradiation damage involves a multi-scale and multi-physics approach, integrating data from experiments, simulations and phenomenological models. This paper focuses on its early stages, specifically neutron-induced collision cascades in zirconium, as nuclear-grade zirconium alloys are widely used in fuel assemblies. We have gathered and analysed a significant sample of results from high-fidelity, large-scale molecular dynamics (MD) simulations, employing existing interatomic potentials and the two-temperature model to account for electron–phonon coupling. Our data can be directly applied to higher-scale methods.

Furthermore, we carried out a comprehensive statistical analysis of the features associated with the defect production, including the number of defects, their distribution and size of the affected area. As a result, we developed a generative model of collision cascades that is parametric, hierarchical and stochastic, i.e. it takes into account a statistical nature of the phenomenon, is interpretable and shareable. This model has been developed with three primary objectives: to provide a sufficient descriptor of a cascade, to interpolate data obtained from high-fidelity simulations, and to demonstrate that the statistical model can produce representative distributions of primary irradiation defects. The results can be used to generate synthetic inputs for models at longer length and time scales, to provide fast approximations that take into account the morphology of introduced defects, and in general to serve as a powerful analytical tool.

Graphene/copper composites are potential structure-function integrated materials. Different from previous micro-nano scale composites, graphene/copper lattice-scale composites and their effects on electrical conductivity are investigated in this work. Using the first-principles calculation to explore the effects of different numbers of graphene layers on the properties of graphene/copper composites, it is found that with the increase of graphene layer numbers, the interfacial bonding strength improves, but the electrical properties show a decreasing trend due to the electron scattering and increasing Cu–C layer distance. The single-layer graphene/copper composite has the most excellent electrical properties, with the highest density of states at the Fermi level and the most charge transfer. A series of layered-structural composites at lattice scale are screened, and their electrical properties and dynamic stability are also predicted. These composites rely on metallic bonding to connect copper and carbon atoms, most of which show obvious vertical charge transfer and high conductivity, with short Cu–C layer distance and alternating arrangement of metal layers and hexagonal carbon atoms. CuC₂ (derived from MgB₂) has the highest average conductivity and dynamic stability and can be considered an ideal lattice-scale composite. The development of new lattice-scale composites is significant for the future research of graphene/copper composites.

Electrocatalyst stability is a key parameter for commercializing high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs). Here, we used density functional theory (DFT) to investigate the stability of Pt-based electrocatalysts by calculating the binding energy (ΔE_b) of surface Pt atoms under working conditions. Our results show that Pt(1 1 1) is more stable than Pt(1 0 0) and Pt(1 1 0) under vacuum conditions. Stress hurts the stability of Pt(1 1 1), regardless of compressive or tensile stress. Most of the transition metals alloyed with Pt improve the stability of Pt(1 1 1), especially PtV, PtY, and PtTa, which increase the ΔE_b by 0.60–0.70 eV (ΔΔE_b). However, the stability of Pt is significantly destroyed under the working conditions of oxygen reduction reaction (ORR). The specific adsorption of ORR intermediates and electrolyte ions decreases the ΔE_b of Pt(1 1 1) by 0.35–0.95 eV, and the effect of PO₄^3-* is more significant. Furthermore, when the electric field of the electrochemical double layer is coupled with PO₄^3-* specific adsorption, ΔΔE_b further increases to 1.12 eV. Our results highlight the importance of the intrinsic ΔE_b and the working conditions for the stability of Pt-based electrocatalysts. This work provides important guidelines for the design of stable ORR electrocatalysts for HT-PEMFCs.

The unique properties of two-dimensional (2D) materials make them highly versatile for a wide range of applications. Recently, low-dimensional structures obtained from bulk non-van der Waals materials have received particular interest. Yttrium carbonate is an example of such materials which hold the potential for creating 2D structures, however, its fundamental properties have been investigated only rarely. In this work, we demonstrate the possibility of obtaining 2D yttrium carbonate with the tengerite-(Y) structure. The electronic and optical properties of both bulk and two-dimensional Y₂(CO₃)₃·2H₂O are investigated using the PBE and HSE06 functionals. While the bulk material is predicted with a bandgap of 7.06 eV at the HSE06 level, the 2D Y₂(CO₃)₃·2H₂O material possesses a bandgap of, untypically, 0.4 eV narrower than the bulk material due to surface effects and different stoichiometry. The optical properties reveal that both the bulk and 2D forms are transparent in the visible and near-UV regions positioning them as promising candidates for various optical applications including doping-induced luminescent devices.

In this work, the effects of refractory transition metal elements on the properties of quaternary equimolar ratio high entropy carbides composed of ⅣB, ⅤB and ⅥB transition metals and C element were studied by first-principles calculations and experiments.The calculation results show that all 75 kinds of high entropy carbides were thermodynamically and mechanically stable and can be formed spontaneously. Among them, Hf can promote the synthesis of high entropy carbides, while Cr has the opposite effect. In addition, single phase solid solution structures can be formed for all 73 HECs except (TiZrHfCr)C and (ZrHfVCr)C. Thereafter, the mechanical properties of these 75 quaternary HECs were compared. The calculation results show that W, Mo, and Ta have a positive effect on improving the elastic modulus, fracture toughness, hardness and melting point of high entropy carbides, while Cr has the opposite effect. The calculation results of phonon spectra show that the six high entropy carbides represented by (TiHfNbTa)C, (TiVNbTa)C, (TiZrNbTa)C, (TiZrNbCr)C, (TiZrNbMo)C and (TiZrNbW)C have lattice dynamic stability. (TiHfNbTa)C, (TiVNbTa)C, (TiZrNbTa)C, (TiZrNbMo)C and (TiZrNbW)C face-centered cubic high entropy carbides were prepared by spark plasma sintering and the microstructure and properties were tested. The experimental results are basically consistent with the calculated results.

The strength of Pb-free solder/Co-Ni alloy joints was investigated using first-principles calculations. The adhesion work was computed, revealing that the inclusion of Ni in the α-CoSn₃ phase reduced the adhesion work for both β-Sn/α-CoSn₃ and Co/α-CoSn₃ systems. Tensile and shear simulations further demonstrated distinct differences in the mechanical properties between β-Sn/α-(Co,Ni)Sn₃ and Co/α-(Co,Ni)Sn₃ systems. The results demonstrate that Co/α-(Co,Ni)Sn₃ exhibited superior tensile and shear strength. The analysis suggests that failure in β-Sn/α-(Co,Ni)Sn₃ structures was more likely to originate either at the interface or near the β-Sn region, as opposed to within the α-(Co,Ni)Sn₃ phase for the Co/α-(Co,Ni)Sn₃ structures. This observation is consistent with predictions made by Griffith’s fracture theory. Additionally, the valence electron density map illustrates the electron transfer under varying tensile strains.