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High entropy alloys (HEA) represent a class of materials with promisingproperties, such as high strength and ductility, radiation damage tolerance,etc. At the same time, a combinatorially large variety of compositions and acomplex structure render them quite hard to study using conventional methods.In this work, we present a computationally efficient methodology based on abinitio calculations within the coherent potential approximation. To make themethodology predictive, we apply an exchange-correlation correction to theequation of state and take into account thermal effects on the magnetic stateand the equilibrium volume. The approach shows good agreement with availableexperimental data on bulk properties of solid solutions. As a particular case,the workflow is applied to a series of iron-group HEA to investigate theirsolid solution strengthening within a parameter-free model based on theeffective medium representation of an alloy. The results reveal intricateinteractions between alloy components, which we analyze by means of a simplemodel of local bonding. Thanks to its computational efficiency, the methodologycan be used as a basis for an adaptive learning workflow for optimal design ofHEA.

Accurate ab initio modeling of solid solution strengthening in high entropy alloys