Quantitative composition‑annealing temperature map of stacking‑fault energy in CoCrNi medium‑entropy alloys unveiled by first‑principles neural‑network potentials
Stacking-fault energy shows a significant variation with composition and annealing temperature, impacting mechanical properties.
The study identifies the optimal conditions for maximizing stacking-fault energy across CoCrNi compositions with variations detailed across temperatures.
Using first-principles neural-network potentials, the analysis delivers a precise map of stacking-fault energy for effective alloy design.
Understanding stacking-fault energy may enable the development of superior medium-entropy alloys for various applications.