About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
2026 Technical Division Student Poster Contest
|
| Presentation Title |
SPG-95: Temperature-Driven Grain-Boundary Segregation and Bulk Demixing in High-Entropy Carbides via Universal MACE Potentials and Hybrid MC–MD Simulations |
| Author(s) |
Marium Mostafiz Mou, Sam Daigle, Donald Brenner, Tarek Haque |
| On-Site Speaker (Planned) |
Marium Mostafiz Mou |
| Abstract Scope |
High-entropy carbides (HECs) are emerging structural ceramics for extreme environments, yet their high-temperature reliability depends critically on grain-boundary (GB) chemistry. Measuring multicomponent segregation across relevant time and temperature scales remains challenging. Here, a universal MACE machine-learning potential is integrated with a hybrid Monte Carlo–molecular dynamics (MC–MD) approach to quantify temperature-dependent GB segregation and bulk stability in six representative HECs. Simulations of a 53.1° symmetric tilt GB from 300–2000 K reveal two regimes: (i) primary-segregant-dominated enrichment (e.g., W,Zr or Mo,Zr) and (ii) multi-element co-segregation with broadened chemical gradients. Elevated temperature consistently thickens the chemically perturbed GB region and promotes disordering, especially in Cr-containing systems. In parallel, bulk MC–MD on (Hf,Mo,V,W,Zr)C shows clear phase separation across the temperature range, confirming intrinsic instability even without GBs. This framework connects atomic-scale segregation and bulk demixing to microstructural degradation pathways, providing design guidance for thermally robust HECs in structural service. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
High-Entropy Alloys, Computational Materials Science & Engineering, Machine Learning |