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Both CALPHAD (CALculation of PHAse Diagrams) and machine-learning (ML) approaches were employed to analyze the phase formation in 2,436 experimentally measured high entropy alloy (HEA) compositions consisting of various quinary mixtures of Al, Co, Cr, Cu, Fe, Mn, and Ni. CALPHAD was found to have good capabilities in predicting the BCC/B2 and FCC phase formation for the 1,761 solid-solution-only compositions, excluding HEAs containing an amorphous phase (AM) or/and intermetallic compound (IM). Phase selection rules were examined systematically using several parameters and it revealed that valency electron concentration (VEC) < 6.87 and VEC > 9.16 are the conditions for the formation of single-phase BCC/B2 and FCC, respectively; and CALPHAD could predict this with essentially 100% accuracy. Both CALPHAD predictions and experimental observations show that more BCC/B2 alloys are formed over FCC alloys as the atomic size difference between the elements increases. Four machine learning (ML) algorithms, decision tree (DT), k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN), were employed to study the phase selection rules for two different datasets, one consisting of 1,761 solid-solution (SS) HEAs without AM and/or IM phases, and the other set consisting of all the 2,436 HEA compositions. Cross validation (CV) was performed to optimize the ML models and the CV accuracies are found to be 91.4%, 93.1%, 90.2%, 89.1% for DT, KNN, SVM, and ANN respectively in predicting the formation of BCC/B2, BCC/B2 + FCC, and FCC; and 93.6%, 93.3%, 95.5%, 92.7% for DT, KNN, SVM, and ANN respectively in predicting SS, AM, SS + AM, and IM phases. Sixty-six experimental bulk alloys with SS structures are predicted with trained ANN model, and the accuracy reaches 81.8%. VEC is found to be most important parameter in phase prediction for BCC/B2, BCC/B2 + FCC, and FCC phases. Electronegativity difference and FCC-BCC-index (FBI) are the two additional dominating features in determining the formation of SS, AM, SS + AM, and IM. A separation line ΔH_mix=28.97×VEC-246.77 was found in the VEC-vs-ΔH_mix plot to predict the formation of single-phase BCC/B2 or FCC with a 96.2% accuracy (ΔH_mix = mixing enthalpy). These insights will be very valuable for designing HEAs with targeted crystal structures.
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Role of Al additions in secondary phase formation in CoCrFeNi high entropy alloys
AlxCoCrFeNi High Entropy Alloys (HEAs), also referred to as multiprincipal element alloys, have attracted significant interest due to their promising mechanical and structural properties. Despite these attributes, AlxCoCrFeNi HEAs are susceptible to phase separation, forming a wide range of secondary phases upon aging, including NiAl–B2 and Cr-rich phases. Controlling the formation of these phases will enable the design of age-hardenable alloys with optimized corrosion resistance. In this study, we examine the critical role of Al additions and their concentration on the stability of the CoCrFeNi base alloy, uncovering the connections between Al composition and the resulting microstructure. Addition of 0.1 mol fraction of Al destabilizes the single-phase microstructure and results in the formation of Cr-rich body-centered-cubic (bcc) phases. Increasing the composition of Al (0.3–0.5 mol fraction) results in the formation of more complex coprecipitates, NiAl–B2 and Cr-rich bcc. Interestingly, we find that the increase of the Al content stimulates the formation of NiAl–B2 phases, increases the overall density of secondary phases, and influences the content of Cr in Cr-rich bcc phases. Density functional theory calculations of simple decomposition reactions of AlxCoCrFeNi HEAs corroborate the tendency for precipitate formation of these phases upon increased Al composition. Additionally, these calculations support previous results, indicating the base CoCrFeNi alloy to be unstable at low temperature. This work provides a foundation for predictive understanding of phase evolution, opening the window toward designing innovative alloys for targeted applications.
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- Award ID(s):
- 1809640
- PAR ID:
- 10594691
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- APL Materials
- Volume:
- 10
- Issue:
- 10
- ISSN:
- 2166-532X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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