About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
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Advances in Multi-Principal Element Alloys V: Mechanical Behavior
|
| Presentation Title |
AI-Enabled Multiscale Design of Ultrastrong Refractory Alloys |
| Author(s) |
Jierui Zhao, Pravendra Patel, Merbin John, Yizhao Wang, Feng Yan, Yufeng Zheng, Yang Chen, Liang Qi |
| On-Site Speaker (Planned) |
Liang Qi |
| Abstract Scope |
We present recent progress in developing an AI-enabled, automated research workflow to accelerate the discovery of ultrastrong metallic alloys. Our approach integrates machine-learning-guided yield strength models, atomistic simulations, and experimental validation to identify and optimize the yield strengths and hardness of multicomponent BCC and BCC-B2 microstructures. Currently, we focus on exploring refractory alloy systems such as W-Ta-Mo and W-Ta-Mo-Nb-V, combining DFT, cluster expansion, and kinetic Monte Carlo simulations to investigate phase stability and ordering kinetics. Experiments using arc melting and flash Joule heating synthesize designed alloys, which are characterized via advanced microscopy and nano-indentation to correlate mechanical behavior with local structures and compositions. Additionally, we model screw dislocation motion using molecular dynamics and energy barrier calculations in chemically complex environments, developing AI-enabled multiscale models of critical resolved shear stress. This integrated framework bridges predictive modeling and high-throughput experimentation, providing new pathways for the physics-informed, AI-accelerated design of next-generation high-performance alloys. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
High-Entropy Alloys, Mechanical Properties, Modeling and Simulation |