Accelerated development of hard high-entropy alloys with data-driven high-throughput experiments
Yi Liu , Jiong Wang , Bin Xiao , Jintao Shu
Journal of Materials Informatics ›› 2022, Vol. 2 ›› Issue (1) : 3
The development of multicomponent alloys with target properties poses a significant challenge, owing to the enormous number of potential component combinations, high costs and the inefficiency of conventional empirical trial-and-error experimental approaches. To tackle this challenge, we develop a machine learning (ML)-guided high-throughput experimental (HTE) approach to accelerate the development of non-equimolar hard
High-throughput experiments / machine learning / multicomponent alloys / high-entropy alloys / hard alloys
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