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Research Highlight

New model predicts formation of stable high-entropy alloys

Researchers devised a model that can predict which combinations of 5 or more elements will form new “high-entropy alloys.” This work, which utilizes values obtained from data mining of high-throughput calculations of binary compounds, requires no experimental or empirically derived input and advances capabilities for “materials by design.” 

The  model correctly identifies all known single-phase alloys while rejecting similar elemental combinations known to form multi-phase alloys. Data mining was used to generate an enthalpy matrix—simple and easy to use—that is associated with binary alloy mixing tendencies.  The model offers flexibility for adding (or customizing) different search criteria for new alloys, such as a desired density range or sorting by price per kg. In addition, the enthalpy matrix can be used to search for specific elemental additions in order to produce a particular second phase with a desired microstructure, for example, improved yield strength. This demonstration of the importance of data mining in materials research may hasten new high-entropy alloys with unusual combinations of strength, ductility, thermal stability, corrosion, and wear resistance.


M. Claudia Troparevsky, James R. Morris, Paul R. C. Kent, Andrew R. Lupini, and G. Malcolm Stocks, “Criteria for predicting the formation of single-phase high-entropy alloys,” Phys. Rev. X 5, 011041 (2015).


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