|About this Abstract
||3rd World Congress on High Entropy Alloys (HEA 2023)
||Multi-scale Modelling of Multiple-principal Element Alloys: From Electrons to Atoms to Continuum using Machine Learning
||Shyue Ping Ong, Hui Zheng, Lauren T. W. Fey, Irene Beyerlein
|On-Site Speaker (Planned)
||Shyue Ping Ong
Refractory multi-principal element alloys (RMPEAs) are promising for high-temperature structural applications. In this talk, I will discuss how multi-scale modeling of RMPEAs from electrons to atoms to continuum can be achieved with recent advances in machine learning (ML). Using the MoNbTi and TaNbTi RMPEAs as examples, we investigate the role of short-range ordering (SRO) on dislocation glide. An accurate ML interatomic potential (MLIP) was developed using DFT calculations. Monte Carlo/molecular dynamics simulations with the MLIP show that MoNbTi exhibits a much greater degree of SRO than TaNbTi, and the local composition directly affects the unstable stacking fault energies (USFEs). These atomistic simulations were then used to parameterize a phase-field dislocation dynamics (PFDD) model. From PFDD simulations, we find that the gliding dislocations experience significant hardening due to pinning and depinning caused by random compositional fluctuations, with higher SRO decreasing the degree of USFE dispersion and hence, the amount of hardening.
||Planned: Metallurgical and Materials Transactions