Abstract
Refractory high-entropy alloys (RHEAs) are promising high-temperature structural materials. Their large compositional space poses great design challenges for phase control and high strength-ductility synergy. The present research pioneers using integrated high-throughput machine learning with Monte Carlo simulations supplemented by ab initio calculations to effectively navigate phase selection and mechanical property predictions, developing single-phase ordered B2 aluminum-enriched RHEAs (Al-RHEAs) demonstrating high strength and ductility. These Al-RHEAs achieve remarkable mechanical properties, including compressive yield strengths up to 1.7 gigapascals, fracture strains exceeding 50%, and notable high-temperature strength retention. They also demonstrate a tensile yield strength of 1.0 gigapascals with a ductility of 9%, albeit with B2 ordering. Furthermore, we identify valence electron count domains for alloy ductility and brittleness with the explanation from density functional theory and provide crucial insights into elemental influence on atomic ordering and mechanical performance. The work sets forth a strategic blueprint for high-throughput alloy design and reveals fundamental principles governing the mechanical properties of advanced structural alloys.
Original language | English |
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Article number | eadq0083 |
Journal | Science Advances |
Volume | 10 |
Issue number | 49 |
DOIs | |
State | Published - Dec 6 2024 |
Funding
M.W. acknowledges a discussion with Amit Samanta on the link between the Fermi level density of states and ductility. Funding: This work was supported by the Department of Energy under grant no. DE-SC0014506 (M.W.), the Office of Naval Research under grant no. N00014-23-1-2441 (J.Q., D.I.H., and J.P.), the National Science Foundation (DMR\u20141611180, 1809640, and 2226508) (X.F. and P.K.L.), the US Army Research Office (FA9550-23-1-0503, W911NF-13-1-0438, and W911NF-19-2-0049) (X.F. and P.K.L.), the Department of Energy (DOE DE-EE0011185) (P.K.L.), and the State of Tennessee and Tennessee Higher Education Commission (THEC) through their support of the Center for Materials Processing (CMP) at the University of Tennessee (X.F.). The present research used the resources of the National Energy Research Scientific Computing Center (NERSC), a US Department of Energy Office of Science User Facility operated under contract number DE-AC02-05CH11231, using the NERSC award, BES-ERCAP24744. (M.W.).