review · Advanced Engineering Materials
Designing high-entropy alloys requires navigating extensive composition ranges, complex phase stability predictions, and specialised manufacturing techniques. This review examines how combining theoretical guidelines, thermodynamic properties, and computational modelling tools, particularly artificial intelligence, supports the design process. It explores various strategies for developing these alloys while evaluating their advantages and limitations. A specific focus is placed on using machine learning techniques to forecast elastic characteristics, detailing both the technical hurdles and prospective solutions involved. Moving forward, the field is expected to rely on collaborative, data-driven approaches where machine learning guides alloy development, provided that these methods are balanced with ethical considerations and supported by continuous experimental validation.
High-entropy alloys offer advanced performance capabilities, but discovering viable compositions by conventional trial and error is slow and expensive. Applying artificial intelligence and predictive modelling allows researchers to rapidly screen materials and predict essential properties, accelerating the development of advanced materials for demanding industrial environments.
This work informs materials engineers and industrial research teams looking to accelerate alloy discovery using predictive artificial intelligence tools, particularly for estimating elastic properties. As a review paper focused on computational methodologies and design frameworks, the discussed approaches remain at an early, research-oriented stage, requiring ongoing experimental validation before direct industrial implementation.
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This review explores the complex process of designing high‐entropy alloys by combining theoretical guidelines, thermodynamic characteristics, and several modeling tools, including artificial intelligence approaches. It tackles issues in the design of high‐entropy alloys, emphasizing the wide composition range, difficulty in forecasting phase stability, and requirement for specialized production techniques. The investigation expands on strategies for creating high‐entropy alloys, emphasizing their benefits and limitations. This article discusses machine learning applications for predicting elastic characteristics, as well as the accompanying challenges and solutions. The future scenario predicts a collaborative world in which machine learning plays a critical role in the data‐driven alloy design of high‐entropy alloys, emphasizing ethical considerations and continual experimental validation for practical advances across industries.
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DOI: 10.1002/adem.202402504
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