Researchers at Skoltech and MIPT have made a breakthrough in alloy discovery, developing a machine learning-based method that significantly speeds up the process of identifying promising metal alloy compositions for lab testing. This innovation promises to revolutionize the traditionally slow and complex process of alloy modeling, offering a more efficient way to find high-performance materials for a wide range of industrial applications.
High-entropy alloys (HEAs) have attracted significant attention in materials science due to their ability to remain stable across a variety of compositions. However, their complexity, with numerous elements and potential configurations, makes the solid solution phase more energy-efficient and favorable, yet also increases the number of competing intermetallic compounds. Understanding and identifying these compounds is crucial for effectively studying and utilizing high-entropy alloys.
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