About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Artificial Intelligence Applications in Integrated Computational Materials Engineering (AI-ICME)
|
| Presentation Title |
H-14: Ionic Transport Properties and Phase Stability of Solid Electrolyte Material Li7La3Zr2O12: A Deep-Neural-Network Molecular Dynamics Investigation |
| Author(s) |
Chunxu Wang, Haoran Cui, Yan Wang, Lei Cao |
| On-Site Speaker (Planned) |
Chunxu Wang |
| Abstract Scope |
All-solid-state batteries (ASSBs) represent a transformative direction for next-generation energy storage, offering enhanced safety, energy density, and thermal stability compared to conventional liquid-electrolyte batteries. Among various solid electrolytes, Li₇La₃Zr₂O₁₂ (LLZO) and its doped variants have emerged as promising candidates due to their relatively high ionic conductivity and chemical stability. However, challenges remain in fully understanding and controlling the nanoscale structural evolution, ion transport mechanisms, and phase stability of these materials, hindering the path toward commercial-scale ASSB deployment. In this work, we present a comprehensive atomic-scale study of LLZO and Al-doped LLZO using deep neural network (DNN) potential-based molecular dynamics (MD). Notably, we have developed and validated high-fidelity DNN interatomic potentials trained on extensive datasets from ab initio molecular dynamics, capturing a wide range of thermodynamic states and compositional variations, including lithium, aluminum, and oxygen environments. We uncover a temperature-dependent phase transition in LLZO that is significantly influenced by aluminum doping. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, Phase Transformations |