Mining of lattice distortion, strength, and intrinsic ductility of refractory high entropy alloys
Abstract Severe lattice distortion is a prominent feature of high-entropy alloys (HEAs) considered a reason for many of those alloys’ properties. Nevertheless, accurate characterizations of lattice distortion are still scarce to only cover a tiny fraction of HEA’s giant composition space due to the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-04-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-023-00993-x |
| Summary: | Abstract Severe lattice distortion is a prominent feature of high-entropy alloys (HEAs) considered a reason for many of those alloys’ properties. Nevertheless, accurate characterizations of lattice distortion are still scarce to only cover a tiny fraction of HEA’s giant composition space due to the expensive experimental or computational costs. Here we present a physics-informed statistical model to efficiently produce high-throughput lattice distortion predictions for refractory non-dilute/high-entropy alloys (RHEAs) in a 10-element composition space. The model offers improved accuracy over conventional methods for fast estimates of lattice distortion by making predictions based on physical properties of interatomic bonding rather than atomic size mismatch of pure elements. The modeling of lattice distortion also implements a predictive model for yield strengths of RHEAs validated by various sets of experimental data. Combining our previous model on intrinsic ductility, a data mining design framework is demonstrated for efficient exploration of strong and ductile single-phase RHEAs. |
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| ISSN: | 2057-3960 |