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Refractory complex concentrated alloys (RCCAs) are a relatively new class of materials that can exhibit excellent mechanical properties at high temperatures, and determining their melting temperature (Tm) is critical to assess their range of operation. Unfortunately, the experimental determination of this property is challenging and computational tools to predict the Tm of RCCAs from first-principles calculations are highly desirable. We quantify the uncertainties associated with such predictions for two methods that can be used with density functional theory-based molecular dynamics and apply them to predict the melting temperature of equiatomic NbMoTaW. We find that a combination of free energy calculations of individual phases with a dynamical coexistence method can provide accurate results with the minimum possible computational cost. We predict the melting temperature for the RCCA NbMoTaW to be between 3000 and 3100 K. 

Complex-concentrated-alloys (CCAs) are of interest for a range of applications due to a host of desirable properties, including high-temperature strength and tolerance to radiation damage. Their multi-principal component nature results in a vast number of possible atomic environments with the associated variability in chemistry and structure. This atomic-level variability is central to the unique properties of these alloys but makes their modeling challenging. We combine atomistic simulations using many body potentials with machine learning to develop predictive models of various atomic properties of CrFeCoNiCu-based CCAs: relaxed vacancy formation energy, atomic-level cohesive energy, pressure, and volume. A fingerprint of the local atomic environments is obtained combining invariants associated with the local atomic geometry and periodic-table information of the atoms involved. Importantly, all descriptors are based on the unrelaxed atomic structure; thus, they are computationally inexpensive to compute. This enables the incorporation of these models into macroscopic simulations. The models show good accuracy and we explore their ability to extrapolate to compositions and elements not used during training.