| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advanced Materials for Energy Conversion and Storage 2024
|
| Presentation Title |
Predicting Short-range Ordering in High Entropy Li-oxides using Density Functional Theory and Crystal Graph Neural Networks |
| Author(s) |
R. Seaton Ullberg, John D. Langhout, Megan M. Butala, Simon R. Phillpot |
| On-Site Speaker (Planned) |
R. Seaton Ullberg |
| Abstract Scope |
The cathodes in commercial Li-ion batteries increasingly have issues of cost and availability. Novel materials for energy storage offer more viable alternatives, such as Li-based cation-disordered rocksalts (DRX). Recent work has shown that DRX is promising due to its high capacity and vast compositional space, which can be exploited to tune performance. However, short-range ordering on the cation sublattice can impede Li-ion transport, which poses a challenge to achieving the theoretical performance. In this work, we use density functional theory to evaluate the formation energy of DRX with different distributions of Li. We use models with Li occupancy localized to low-index planes to represent short-range order, and special quasirandom structures to represent total disorder. Variants in the Li1.2Mn0.4Ti0.4-XZrXO2 compositional space are investigated to determine the critical factors driving short-range ordering. In addition, we use crystal graph neural networks to assist with the computationally expensive task of screening diverse DRX compositions. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Energy Conversion and Storage, Computational Materials Science & Engineering, |