About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Late News Poster Session
|
| Presentation Title |
H-6: Assessing Strengthening Mechanisms and Machine-Learned Potentials in Refractory High-Entropy Alloys |
| Author(s) |
Lassi Linnala, Wenqing Wang, Tatu Pinomaa, Anssi Laukkanen, Mark Asta, Satish Rao |
| On-Site Speaker (Planned) |
Lassi Linnala |
| Abstract Scope |
Refractory high-entropy alloys (RHEAs) exhibit high retained strength, where both screw and edge dislocations can contribute comparably to strengthening, unlike in elemental BCC metals or their dilute alloys. Theoretical models developed by Maresca–Curtin and Rao–Suzuki describe edge- and screw-mediated strengthening, respectively. The latter relies on dislocation core-solute interaction energies that have been difficult to determine accurately for complex concentrated alloys. Recent advancements in machine-learned interatomic potentials (MLIPs) now enable their evaluation. Here, we assess the accuracy of several existing MLIPs in predicting yield strengths using the Rao–Suzuki screw theory and compare these predictions with experimental data and with Maresca–Curtin edge theory results for the prototypical RHEAs MoNbTaW and MoNbTaVW, where V additions could promote edge over screw strengthening. These comparisons benchmark the performance of current MLIPs, guiding their development, and demonstrate the applicability of both strengthening theories for computational design of high-temperature RHEAs exploiting screw- and edge-mediated strengthening. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Computational Materials Science & Engineering, High-Entropy Alloys, |