Machine learning and high-throughput computational guided development of high temperature oxidation-resisting Ni-Co-Cr-Al-Fe based high-entropy alloys

Abstract

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.