| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Local Chemical Ordering and Its Impact on Mechanical Behaviors, Radiation Damage, and Corrosion
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| Presentation Title |
Predicting short-range order in complex concentrated alloys - A tale of DFT and data-driven approaches |
| Author(s) |
Prashant Singh, Duane D. Johnson, Hailong Huang, Gaoyuan Ouyang, Nicolas Argibay, Rameshwari Naorem, Ryan Ott, Rajarshi Banerjee, Soni Vishal, Pratik Ray, Dishant Beniwal |
| On-Site Speaker (Planned) |
Prashant Singh |
| Abstract Scope |
Short-range ordering (SRO) is observed experimentally in complex concentrated alloys (CCAs), yet, the interpretation of the pair-correlations remains a challenge to both theory and experiments. This presentation focuses on our recent developments of density-functional-theory (DFT) based Landau-type description at finite-temperatures to investigate SRO in CCAs, in particular, how to interpret uniquely the SRO. We will briefly discuss how DFT can be wrapped with AI/ML approaches to create an automated workflow to quantitatively predict SRO in CCAs. Finally, the DFT and SRO theories were successfully combined with AI/ML to develop compositional map of phase-stability, intrinsic-strength, and chemical ordering for body-centered-cubic (bcc) refractory CCAs, which provided crucial guidelines to design single-phase alloys with an exceptional combination of RT tensile ductility and strength. Atom-probe tomography (APT) and transmission electron-microscopy (TEM) were used to validate SRO, and better understand the link with measured experimental strength, and tensile ductility. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Modeling and Simulation, Machine Learning, High-Temperature Materials |