A review on high-throughput development of high-entropy alloys by combinatorial methods

Abstract

High-entropy alloys (HEAs) are an emerging class of alloys with multi-principal elements that greatly expands the compositional space for advanced alloy design. Besides chemistry, processing history can also affect the phase and microstructure formation in HEAs. The number of possible alloy compositions and processing paths gives rise to enormous material design space, which makes it challenging to explore by traditional trial-and-error approaches. This review highlights the progress in combinatorial high-throughput studies towards rapid prediction, manufacturing, and characterization of promising HEA compositions. This review begins with an introduction to HEAs and their unique properties. Then, this review describes high-throughput computational methods such as machine learning that can predict desired alloy compositions from hundreds or even thousands of candidates. The next section presents advances in combinatorial synthesis of material libraries by additive manufacturing for efficient development of high-performance HEAs at bulk scale. The final section discusses the high-throughput characterization techniques used to accelerate the material property measurements for systematic understanding of the composition-processing-structure-property relationships in combinatorial HEA libraries.

Keywords

High-entropy alloys / machine learning / combinatorial studies / high throughput / additive manufacturing / alloy design

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