RESUMEN
High-entropy alloys are solid solutions of multiple principal elements that are capable of reaching composition and property regimes inaccessible for dilute materials. Discovering those with valuable properties, however, too often relies on serendipity, because thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. We propose an active learning strategy to accelerate the design of high-entropy Invar alloys in a practically infinite compositional space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys out of millions of possible compositions, we identified two high-entropy Invar alloys with extremely low thermal expansion coefficients around 2 × 10-6 per degree kelvin at 300 kelvin. We believe this to be a suitable pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic, and electrical properties.
RESUMEN
We introduce an efficient, automated computational approach for analyzing interfaces within atom probe tomography datasets, enabling quantitative mapping of their thickness, composition, as well as the Gibbsian interfacial excess of each solute. Detailed evaluation of an experimental dataset indicates that compared with the composition map, the interfacial excess map is more robust and exhibits a relatively higher resolution to reveal compositional variations. By field evaporation simulations with a predefined emitter mimicking the experimental dataset, the impact of trajectory aberrations on the measurement of the thickness, composition, and interfacial excess of the decorated interface are systematically analyzed and discussed.
RESUMEN
Severe lattice distortion is a core effect in the design of multiprincipal element alloys with the aim to enhance yield strength, a key indicator in structural engineering. Yet, the yield strength values of medium- and high-entropy alloys investigated so far do not substantially exceed those of conventional alloys owing to the insufficient utilization of lattice distortion. Here it is shown that a simple VCoNi equiatomic medium-entropy alloy exhibits a near 1 GPa yield strength and good ductility, outperforming conventional solid-solution alloys. It is demonstrated that a wide fluctuation of the atomic bond distances in such alloys, i.e., severe lattice distortion, improves both yield stress and its sensitivity to grain size. In addition, the dislocation-mediated plasticity effectively enhances the strength-ductility relationship by generating nanosized dislocation substructures due to massive pinning. The results demonstrate that severe lattice distortion is a key property for identifying extra-strong materials for structural engineering applications.
