Ni-Co-Cr-Al-Fe-based high-entropy alloys(HEAs)have been demonstrated to possess exceptional oxidation resistance,rendering them promising candidates as bond coats to protect critical components in turbine power system...Ni-Co-Cr-Al-Fe-based high-entropy alloys(HEAs)have been demonstrated to possess exceptional oxidation resistance,rendering them promising candidates as bond coats to protect critical components in turbine power systems.However,with the conventional time-consuming alloy design approach,only a small fraction of Ni-Co-Cr-Al-Fe-based HEAs,focusing on equiatomic compositions,has been explored to date.In this study,we developed an effective design framework with the aid of machine learning(ML)and high throughput computations,enabling the rapid exploration of high-temperature oxidation-resistant non-equiatomic HEAs.This innovative approach leverages ML techniques to swiftly select candidates with superior oxidation resistance within the expansive high-entropy composition landscape.Complemented by a thermodynamic-informed ranking-based selection process,several novel non-equiatomic Ni-Co-Cr-Al-Fe HEA candidates surpassing the oxidation resistance of the state-of-the-art bond coat material MCrAlY have been identified and further experimentally demonstrated.Our findings offer a pathway for the development of advanced bond coats in the realm of next-generation turbine engine technology.展开更多
基金supported by the U.S.Department of Energy through the award#DE-EE0010214We would like to thank the Technology Manager Christian Rawson and Project manager Nick Lalena for the technical guidance and financial support.The computational time provided by the Thorny Flat High-Performance Computer Cluster is highly acknowledged.
文摘Ni-Co-Cr-Al-Fe-based high-entropy alloys(HEAs)have been demonstrated to possess exceptional oxidation resistance,rendering them promising candidates as bond coats to protect critical components in turbine power systems.However,with the conventional time-consuming alloy design approach,only a small fraction of Ni-Co-Cr-Al-Fe-based HEAs,focusing on equiatomic compositions,has been explored to date.In this study,we developed an effective design framework with the aid of machine learning(ML)and high throughput computations,enabling the rapid exploration of high-temperature oxidation-resistant non-equiatomic HEAs.This innovative approach leverages ML techniques to swiftly select candidates with superior oxidation resistance within the expansive high-entropy composition landscape.Complemented by a thermodynamic-informed ranking-based selection process,several novel non-equiatomic Ni-Co-Cr-Al-Fe HEA candidates surpassing the oxidation resistance of the state-of-the-art bond coat material MCrAlY have been identified and further experimentally demonstrated.Our findings offer a pathway for the development of advanced bond coats in the realm of next-generation turbine engine technology.
