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The high entropy alloys have become the most intensely researched materials in recent times. They offer the flexibility to choose a large array of metallic elements in the periodic table, a combination of which produces distinctive desirable properties that are not possible to be obtained by the pristine metals. Over the past decade, a myriad of publications has inundated the aspects of materials synthesis concerning HEA. Hitherto, the practice of HEA development has largely relied on a trial-and-error basis, and the hassles associate with this effort can be reduced by adopting a machine learning approach. This way, the “right first time” approach can be adopted to deterministically predict the right combination and composition of metallic elements to obtain the desired functional properties. This article reviews the latest advances in adopting machine learning approaches to predict and develop newer compositions of high entropy alloys. The review concludes by highlighting the newer applications areas that this accelerated development has enabled such that the HEA coatings can now potentially be used in several areas ranging from catalytic materials, electromagnetic shield protection and many other structural applications.

Emergence of machine learning in the development of high entropy alloy and their prospects in advanced engineering applications