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The mechanical behaviour of steels is strongly related to their underlying atomistic structures which evolve during thermal treatment. Cu-alloyed α-Fe undergoes a change in material behaviour during the ageing process, especially at temperatures of above 300°C, where precipitates form on a large time-scale within the α-Fe matrix, yielding first a precipitation strengthening of the material. As the precipitates grow further in time, the material strength decreases again. This complex process is modelled with a multiscale approach, combining Kinetic Monte Carlo (KMC) with Molecular Dynamics (MD) simulations in a sequential way and exploiting the advantages of both methods while simultaneously circumventing their particular disadvantages. The formation of precipitates is modelled on a single-crystal lattice with a diffusion based KMC approach. Transferring selected precipitation states at different ageing times to MD simulations allows the performance of nano tensile tests and the analysis of failure initiation. The anisotropic tensile behaviour is investigated in the [100], [110] and [111] directions, showing monotonically decreasing tensile strengths and deformation strains. Hence precipitation strengthening is mainly due to dislocation–precipitate interactions which are non-existent at small tensile loadings in this scenario. At the point of ductile failure, dislocations are generated at the interfaces between precipitates and the Fe matrix. Straining in the [100] direction, they lie on {110} and {112} glide planes, as expected. With the method presented here, the changes of the anisotropic tensile moduli are related to different states of thermal ageing, i.e., to nucleation, growth and Ostwald ripening of Cu precipitates. [ABSTRACT FROM PUBLISHER]

Abstract Copper alloys show a structural variability leading to a range of diverse electronic, transport, and mechanical properties. Here, we investigate the influence of the type and amount of alloying on the electronic transport properties across copper alloys. Specifically, we investigate the electronic transmission along copper in its fcc crystal structure. The characteristic change when adding impurities, such as nickel, aluminum, and silicon in the structure and transport properties is assessed through density functional theory calculations together with the non-equilibrium Greens functions approach. The results are analyzed with respect to the structural characteristics, the electronic and transport properties in these alloys for different concentrations of the impurities. A clear trend in the electronic transmission, thus the conductance, along these materials was found with the addition of certain impurities and their clusters. Coherent with experimental observations, we conclude that the higher the concentration of the impurities, the lower the electronic transmission along the alloys as compared to a pure copper crystal. Furthermore, we show that the bonding environment in the impurity clusters can be associated to additional valence states and significantly influences the conductance. In the end, we discuss the relevance of our results for practical applications. Highlights • Impurities enhance valence band of alloy. • Conductance decrease of Cu alloy pronounced for Si. • Impurity clusters enhance Cu alloy conductance. • High impurity concentration decreases the conductance of Co alloys. [ABSTRACT FROM AUTHOR]

Binary complexes that can be found in copper alloys are investigated in this work through a combination of computer simulations and machine learning. Copper alloys are made of a copper matrix and a combination of single alloying elements in n -ary forms. Due to the coexistence of different types of phases in this matrix, complex regions exist for which information on their precise atomistic structure is missing. In order to uncover such information, we apply active learning and generate moment tensor potentials. This development is based on quantum-mechanical calculations. This approach allows the on-the-fly relaxation of many thousands of potentially novel candidates and check their stability. The ground-state energy of these structures is used to build active learning-generated convex hulls, which are in turn being compared to those from the simulations and the AFLOW database. This procedure provides an insight to additional new stable copper alloy relevant binary complexes. Here, in view of Cu–Ni–Si–Cr alloys, the binary complexes Cu–Si, Ni–Si, Cr–Si, Cr–Ni, Cu–Ni, and Cu–Cr have been investigated. Their stability and the identification of novel stable candidates are discussed based on energetic arguments and the analysis of the respective phonon dispersion. The pipeline followed in this work is able to successfully predict binary phases in Cu–Ni–Si–Cr alloys, specifically for the Cu-Si, Ni–Si, Cr–Ni and Cu–Ni complexes, and to extend the already reported structures in the AFLOW library. In the end, we show the applicability of a predicted Cu–Si stable phase and the developed machine learned potentials at the larger scale of atomistic simulations for the calculation of their mechanical properties and melting behavior. This work provides a computationally efficient framework for material structure prediction and calculation of their properties at a quantum-mechanical accuracy. • Efficient discovery of new binary phases of Cu-Ni-Si-Cr alloys. • Active learning training of moment tensor potentials. • Generation of machine-learned potentials with quantum-mechanical accuracy. [ABSTRACT FROM AUTHOR]

Molecular dynamics simulations are used to investigate tensile strengths and failure mechanisms of aluminium nanopolycrystals with an average grain diameter of 8 nm containing dissolved Ni, Cu, Mg or Ti atoms. It is shown that tensile strengths are influenced by several factors such as stacking fault energies as well as types, concentrations and positions of the dissolved atoms. A strong strengthening was found in case of Cu whereas Ni or Ti lead to a moderate increase of strength, while Mg even lowers the tensile strength. It was found that both dislocation processes as well as grain boundary dominated effects contribute to plastic failure mechanisms. Type and concentration of dissolved atoms possess a significant influence on these failure mechanisms. Whereas alloying with Ni, Cu or Mg supports grain boundary dominated failure processes, dissolved Ti atoms lead to a significant increase of stacking faults and dislocations. [ABSTRACT FROM AUTHOR]

Abstract: The coarsening kinetics of Cu-rich precipitates in an α-Fe matrix for thermally aged Fe–Cu alloys at temperatures above 700°C is studied using a kinetic Monte Carlo (KMC) simulation and a phase-field method (PFM). In this work, the KMC approach adequately captures the early stage of the system evolution which involves nucleation, growth and coarsening, while the PFM provides a suitable framework for studying late-stage coarsening at large precipitate volume fraction regimes. Hence, both models complement each other by transferring the results of KMC along with precipitate–matrix interface energies from a broken-bond model to a quantitative PFM based on a grand chemical potential formulation and the CALPHAD database. Furthermore, molecular dynamics simulations provide information on the structural coherency of the precipitates and hence justify the sequential parameter transfer. We show that our PFM can be validated quantitatively for the Gibbs–Thomson effect and that it also predicts the coarsening kinetics correctly. It is found that the kinetics closely follow the LSW (Lifshitz–Slyozov–Wagner) law, whereas the coarsening rate constant increases with an increase in volume fraction of precipitates. [Copyright &y& Elsevier]