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Hardness is an essential property in the design of refractory high entropy alloys (RHEAs). This study shows how a neural network (NN) model can be used to predict the hardness of a RHEA, for the first time. We predicted the hardness of several alloys, including the novel C0.1Cr3Mo11.9Nb20Re15Ta30W20 using the NN model. The hardness predicted from the NN model was consistent with the available experimental results. The NN model prediction of C0.1Cr3Mo11.9Nb20Re15Ta30W20 was verified by experimentally synthesizing and investigating its microstructure properties and hardness. This model provides an alternative route to determine the Vickers hardness of RHEAs.

We report results from ab-initio, self-consistent density functional theory (DFT) calculations of electronic, transport and bulk properties of rock salt magnesium sulfide (MgS). In the absence of experimental data on these properties, except for the bulk modulus, these results are predictions. Our calculations utilized the Ceperley and Alder local density approximation (LDA) potential and the linear combination of Gaussian orbitals (LCGO).The key difference between our computations and other previous ab-initio DFT ones stems from our use of successively larger basis sets, in consecutive, self-consistent calculations, to attain the ground state of the material. We predicted an indirect (Γ-X) band gap of 3.278 eV for a room temperature lattice constant of 5.200Å. We obtained a predicted low temperature indirect (Γ-X) band gap of 3.512 eV, using the equilibrium lattice constant of 5.183Å. We found a theoretical value of 79.76 GPa for the bulk modulus; it agrees very well with the experimental finding of 78 ± 3.7 GPa.