![]() ![]() Numerous studies have revealed a wide variety of structural and functional properties 2, 3, 4, 5, 6, 7 in this large space of materials, ranging from cryogenic ductility 8, high strength 9, 10, corrosion resistance 11, 12, to excellent wear behavior 13, and thermoelectric properties 14. ![]() The term “high entropy” was coined based on the idea that a single solid-solution phase can be stabilized with a high configurational entropy associated with the random mixing of multiple elements at similar atomic fractions 2. High-entropy alloys (HEA) are a new class of multi-principal element materials with diverse and fascinating structure-property relationships 1. Association rule mining was applied to the model predictions to describe the compositional dependence of HEA elastic properties and to demonstrate the potential for data-driven alloy design. The Deep Sets model has better predictive performance and generalizability compared to other ML models. The elastic property dataset was used to train a ML model with the Deep Sets architecture. In this work, the EMTO-CPA method was used to generate a large HEA dataset (spanning a composition space of 14 elements) containing 7086 cubic HEA structures with structural properties, 1911 of which have the complete elastic tensor calculated. ![]() Machine learning (ML) methods might address this challenge, but ML of HEAs has been hindered by the scarcity of HEA property data. High entropy alloys (HEAs) are an important material class in the development of next-generation structural materials, but the astronomically large composition space cannot be efficiently explored by experiments or first-principles calculations. ![]()
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