| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
High Entropy Materials: Concentrated Solid Solutions, Intermetallics, Ceramics, Functional Materials and Beyond V
|
| Presentation Title |
Mining Lattice Distortion, Strength, and Intrinsic Ductility of Refractory High-Entropy Alloys Using Physics-Informed Statistical Learning |
| Author(s) |
Yong-Jie Hu, Christopher Tandoc |
| On-Site Speaker (Planned) |
Yong-Jie Hu |
| Abstract Scope |
Severe lattice distortion is a prominent feature of high-entropy alloys (HEAs) considered a reason for many of those alloys’ unique 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. |