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In order to efficiently explore the nearly infinite composition space in multicomponent solid solution alloys, it is important to establish predictive design strategies and use computation-aided methods. In the present work, we demonstrated the density functional theory calculations informed design routes for realizing transformation-induced plasticity (TRIP) and twinning-induced plasticity (TWIP) in Cr-Co-Ni medium entropy alloys (MEAs). We systematically studied the effects of magnetism and chemical composition on the generalized stacking fault energy surface (gamma-surface) and showed that both chemistry and the coupled magnetic state strongly affect the gamma-surface, consequently, the primary deformation modes. Based on the calculated effective energy barriers for the competing deformation modes, we constructed composition and magnetism dependent deformation maps at both room and cryogenic temperatures. Accordingly, we proposed various design routes for achieving desired primary deformation modes in the ternary Cr-Co-Ni alloys. The deformation mechanisms predicted by our theoretical models are in nice agreement with available experimental observations in literature. Furthermore, we fabricated two non-equiatomic Cr-Co-Ni MEAs possessing the designed TWIP and TRIP effects, showing excellent combinations of tensile strength and ductility.
We have investigated the plastic deformation properties of non-equiatomic single phase Zr-Nb-Ti-Ta-Hf high-entropy alloys from room temperature up to 300 {deg}C. Uniaxial deformation tests at a constant strain rate of 10$^{-4}$ s$^{-1}$ were performe
High-entropy alloys (HEAs) are solid solutions of multiple elements with equal atomic ratios which present an innovative pathway for de novo alloy engineering. While there exist extensive studies to ascertain the important structural aspects governin
We demonstrate by means of fully relativistic first principles calculations that, by substitution of Fe by Cr, Mn, Co, Ni or Cu in FePt-L10 bulk alloys, with fixed Pt content, it is possible to tune the magnetocrystalline anisotropy energy by adjusti
Neural populations exposed to a certain stimulus learn to represent it better. However, the process that leads local, self-organized rules to do so is unclear. We address the question of how can a neural periodic input be learned and use the Differen
We here show by first principles theory that it is possible to achieve a structural and magnetic phase transition in common steel alloys like Fe$_{85}$Cr$_{15}$, by alloying with Ni or Mn. The predicted phase transition is from the ferromagnetic body