About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Artificial Intelligence Applications in Integrated Computational Materials Engineering (AI-ICME)
|
| Presentation Title |
Toward DFT-Accurate Modeling of HfNbTaTiZr High Entropy Alloy Using Moment Tensor Potential |
| Author(s) |
Mashroor Shafat Nitol, Avanish Mishra, Shuozhi Xu, Saryu Fensin |
| On-Site Speaker (Planned) |
Mashroor Shafat Nitol |
| Abstract Scope |
High entropy alloys (HEAs) such as HfNbTaTiZr offer exceptional mechanical properties and thermal stability due to their complex atomic interactions and local chemical environments. Accurate modeling of these materials remains challenging, particularly for predicting generalized stacking fault energies (GSFE) and short-range order (SRO), which are critical to understanding deformation mechanisms. In this work, a Moment Tensor Potential (MTP) is developed for HfNbTaTiZr to achieve near-DFT accuracy in GSFE and SRO, while preserving the fidelity of pure-element properties. Classical MEAM potentials were found to inaccurately predict both SRO trends and stacking fault energetics. In contrast, the machine-learned MTP captures the configurational complexity of the alloy and enables efficient atomistic simulations of defects and dislocations. This potential addresses the shortcomings of classical methods and provides a reliable framework for investigating mechanical behavior in multi-principal element alloys, supporting accelerated materials design through integration with ICME approaches. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Modeling and Simulation, Machine Learning, ICME |