The phase stability of a bcc AlNbTiV high-entropy alloy at elevated temperatures is studied using a combination of machine-learning interatomic potentials, first-principles calculations, and Monte Carlo simulations. The simulations reveal a B2 ordering below about 1700 K, mainly caused by a strong site preference of Al and Ti. A much weaker site preference for V and Nb is observed, strongly affecting the alloys total configurational entropy. The underlying mechanisms of the B2 phase stability as opposed to the random solid solution are discussed in terms of a high persisting configurational entropy of the B2 phase due to strong sublattice site disorder.

Concentrated solid solutions including the class of high entropy alloys (HEAs) have attracted enormous attention recently. Among these alloys a recently developed face-centered cubic (fcc) equiatomic VCoNi alloy revealed extraordinary high yield strength, exceeding previous high-strength fcc CrCoNi and FeCoNiCrMn alloys. Significant lattice distortions had been reported in the VCoNi solid solution. There is, however, a lack of knowledge about potential short-range order (SRO) and its implications for most of these alloys. We performed first-principles calculations and Monte Carlo simulations to compute the degree of SRO for fcc VCoNi, namely, by utilizing the coherent-potential approximation in combination with the generalized perturbation method as well as the supercell method in combination with recently developed machine-learned potentials. We analyze the chemical SRO parameters as well as the impact on other properties such as relaxation energies and lattice distortions.

Recently, high-entropy alloys (HEAs) have attracted wide attention due to their extraordinary materials properties. A main challenge in identifying new HEAs is the lack of efficient approaches for exploring their huge compositional space. Ab initio calculations have emerged as a powerful approach that complements experiment. However, for multicomponent alloys existing approaches suffer from the chemical complexity involved. In this work we propose a method for studying HEAs computationally. Our approach is based on the application of machine-learning potentials based on ab initio data in combination with Monte Carlo simulations. The high efficiency and performance of the approach are demonstrated on the prototype bcc NbMoTaW HEA. The approach is employed to study phase stability, phase transitions, and chemical short-range order. The importance of including local relaxation effects is revealed: they significantly stabilize single-phase formation of bcc NbMoTaW down to room temperature. Finally, a so-far unknown mechanism that drives chemical order due to atomic relaxation at ambient temperatures is discovered.