Abstract
High entropy alloys (HEA) are a new type of high-performance structural material. Their vast degrees of compositional freedom provide for extensive opportunities to design alloys with tailored properties. However, compositional complexities present challenges for alloy design. Current approaches have shown limited reliability in accounting for the compositional regions of single solid solution and composite phases. For the first time, a phenomenological method analysing binary phase diagrams to predict HEA phases is presented. The hypothesis is that the HEA structural stability is encoded within the phase diagrams. Accordingly, we introduce several phase-diagram inspired parameters and employ machine learning (ML) to classify 600+ reported HEAs based on these parameters. Compared to other large database statistical prediction models, this model gives more detailed and accurate phase predictions. Both the overall HEA prediction and specifically single-phase HEA prediction rate are above 80%. To validate our method, we demonstrated its capability in predicting HEA solid solution phases with or without intermetallics in 42 randomly selected complex compositions, with a success rate of 81%. The presented search approach with high predictive capability can be exploited to interact with and complement other computation-intense methods such as CALPHAD in providing an accelerated and precise HEA design.
Highlights
High entropy alloys (HEA) are a new type of high-performance structural material
Departing from current approaches, we present a phenomenological method using binary phase diagrams to predict the compositional space of HEA phases
The phases studied here are those with homogeneity ranges in the phase diagrams such as body-centred cubic (BCC) single-phase, face-centred cubic (FCC) single-phase, mixed FCC + BCC phase, hexagonal close-packed (HCP) single-phase, Sigma phase, and Laves phase
Summary
A method predicting the phase formation of HEAs based solely on the binary phase diagrams is demonstrated and validated. The information on elemental mixing and phase separation from binary phase diagrams has provided success to the phenomenological approach presented. Compared with the other large database statistical approaches, Tancret et al combined Gaussian Process using nine thermodynamic and atomistic parameters with CALPHAD to predict the formation of over 60 single solid solution phase HEAs25. The βi’s are optimised by ordinal logistic regression based on a database of over 2000 sputter deposited HEAs from a high-throughput experiment[26] This method is efficient in separating out FCC and BCC single phase HEAs. But mixed FCC and BCC phase cannot be separated from the prior phases. Our method is based solely on binary phase diagrams for which there exist plentiful accessible data
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