About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Materials Design and Processing Optimization for Advanced Manufacturing: From Fundamentals to Application
|
| Presentation Title |
Short-range Order and Its Impacts on the BCC NbMoTaW Multi-principal Element Alloy by Machine-learning Potentials |
| Author(s) |
XiaoXiang Yu, Qiang Zhu, YunJiang Wang, Lin Li |
| On-Site Speaker (Planned) |
XiaoXiang Yu |
| Abstract Scope |
Body-centered cubic (BCC) refractory multi-principal element alloys (MPEAs) show exceptional mechanical properties compared to conventional BCC alloys. The mechanistic origin of superior strength is yet under-explored, and the question remains open to the influence of short-range order (SRO) on the strengthening mechanisms.
Here we employ a machine-learning force field trained by a neural network (NN) with bispectrum coefficients as descriptors. The NN interatomic potential provides a transferable force field with density functional theory accuracy. We apply this novel NN potential in the following hybrid molecular dynamics/Monte Carlo simulations to elucidate the complicated interplay between SRO, phase stability, dislocation core structures, plasticity, and strength in the NbMoTaW MPEA. This approach enables rapid high-throughput screening through a vast compositional space, paves the way for computation-guided materials design of new MPEAs with better performance, and opens avenues for tuning the mechanical properties by processing optimization. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
High-Entropy Alloys, Machine Learning, Mechanical Properties |