Impact of three-body interactions in a ReaxFF force field for Ni and Cr transition metals and their alloys on the prediction of thermal and mechanical properties
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Here, we developed a PyTorch-based architecture called HydraGNN that implements graph convolutional neural networks (GCNNs) to predict the formation energy and the bulk modulus for models of solid solution alloys for various atomic crystal structures and relaxed volumes. We trained the GCNN surrogate model on a dataset for nickel–niobium (NiNb) generated by the embedded atom model (EAM) empirical interatomic potential for demonstration purposes. The dataset was generated by calculating the formation energy and the bulk modulus as a prototypical elastic property for optimized geometries starting from initial body-centered cubic (BCC), face-centered cubic (FCC), and hexagonal compact packed (HCP) crystal structures, with configurations spanning the possible compositional range for each of the three types of initial crystal structures. Numerical results show that the GCNN model effectively predicts both the formation energy and the bulk modulus as function of the optimized crystal structure, relaxed volume, and configurational entropy of the model structures for solid solution alloys.
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This paper presents a pixel-based modeling approach of concrete which combines an experimental characterization of concrete and the Fast-Fourier transform simulations. High-resolution phase maps created from experimental characterization by micro X-ray fluorescence, energy dispersive X-ray analysis, and X-ray diffraction contain 9 different phases, including 22.17 vol.% of hydrated cement paste, 54.21 vol.% of minerals, and 23.62 vol.% of interfaces. These phases provide the input for determining the effective elastic properties, coupled with Fast-Fourier transform-based simulation. The simulation results show that the effective range of Young’s modulus of concrete is comparable with the range of experimental values ~ 37 ± 4 GPa with the assumption of realistic properties of interfaces.
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While ordered L12 or gamma prime precipitates in face centered cubic (FCC) based microstructures have been extensively used for strengthening Nickel or Cobalt base superalloys, and more recently in high entropy alloys (HEAs) or complex concentrated alloys (CCAs), the possibility of exploiting ordered B2 precipitates in FCC-based systems has been relatively less investigated. The present study shows the propensity of developing a heterogeneous microstructure, consisting of two different distributions of FCC grain sizes, and two different size scales of B2 precipitates, within an FCC-based Al0.5Co1.5CrFeNi1.5 HEA/CCA. This alloy composition has been designed using solution thermodynamics-based modeling such that it has a high phase fraction and solvus temperature of the B2 phase. The resulting heterogenous microstructure exhibited an approximately 400% increase in yield strength with respect to the single-phase FCC solid solution condition of the same alloy while maintaining very good tensile ductility ~20 %.