About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Thermodynamics and Kinetics
|
| Presentation Title |
Zentropy: Implementing Recursive Entropy for First-Principles Thermodynamics |
| Author(s) |
Nigel Lee En Hew, Luke Allen Myers, Shun-Li Shang, Zi-Kui Liu |
| On-Site Speaker (Planned) |
Nigel Lee En Hew |
| Abstract Scope |
Most first-principles methods, such as density functional theory (DFT), typically neglect intra-configurational entropy and consider only inter-configurational contributions when evaluating the total entropy. However, it is well known that each configuration inherently possesses intra-configurational entropy arising from vibrational and electronic degrees of freedom, which can significantly influence finite-temperature properties. To address this limitation, we apply the zentropy method, which extends recursive-entropy concepts from information theory to configurations encountered in DFT calculations. This approach enables more accurate predictions of emergent finite-temperature phenomena, including negative thermal expansion and pressure–temperature or volume–temperature phase diagrams. An open-source Python package, PyZentropy, is being developed to implement this method, and its capabilities are demonstrated through a case study on Fe₃Pt. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Modeling and Simulation, Phase Transformations |