| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Computational Thermodynamics and Kinetics
|
| Presentation Title |
Phase diagrams and crystallization of molten salts studied by machine-learned potentials |
| Author(s) |
Zhao Fan, Michael Whittaker, Piotr Zarzycki , Mark Asta |
| On-Site Speaker (Planned) |
Zhao Fan |
| Abstract Scope |
Molecular dynamics simulations based on accurate and efficient machine-learning interatomic potentials (MLIPs) trained on Density-Functional-Theory (DFT) calculations are used to model temperature-composition phase diagrams and examine the compositional-dependent crystal nucleation rates of molten salts. The work is motivated by the potential relevance to synthesis pathways for multicomponent battery cathode materials from molten salts. We demonstrate the applications to three binary mixtures (LiF-NaF, LiF-KF, and NaF-KF) employing MLIPs based on the Atomic Cluster Expansion formalism. The potentials are validated against available experimental data, including lattice constant, liquid density, melting point, heat capacity, and mixing enthalpy. We dissect the roles of different factors, such as thermodynamic driving force, interfacial free energy, liquid structure, on variation of crystal nucleation rates. This work was funded by the U.S. DOE-BES Materials Science Division, MINES program. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Solidification, Modeling and Simulation, Machine Learning |