Design of High-Ionic-Conductivity Doped Li2ZrO3 Cathode Coating with Machine Learning Interatomic Potentials
4 affiliations
- 1Aiiso Yufeng Li Family Department of Chemical and NanoEngineering, University of California San Diego, La Jolla, CA, United States
- 2University of California San Diego
- 3Department of Materials Science and Engineering
- 4National University of Singapore
Abstract
Li2ZrO3 (LZO) is a promising cathode coating material for all-solid-state batteries, but its intrinsically low ionic conductivity limits rate performance. Here, we investigate the effects of Y3+, Al3+, Nb5+, and Ta5+ doping and amorphization on the Li+ conductivity of LZO using density functional theory (DFT) and machine learning interatomic potentials (MLIPs). Among the dopants studied, Y3+ substitution exhibits the most favorable defect formation energies. Molecular dynamics simulations using a fine-tuned moment tensor potential (MTP) reveal that 6%-Y-doped crystalline LZO (c-LYZO) achieves a maximum room-temperature ionic conductivity of ∼2mS cm−1, an order of magnitude improvement over undoped LZO. Amorphization was also found to enhance the conductivity of undoped LZO but the effect is attenuated in doped LZO. We show that amorphous LYZO exhibits more spatially fragmented, trapping-dominated Li transport than its crystalline counterpart. These results identify Y-doped LZO as a potential high-conductivity cathode coating and provide a design framework that balances dopant chemistry, defect type, doping concentration, and structural order.
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BibTeX
@misc{wang2026design,
title = {{Design of High-Ionic-Conductivity Doped Li2ZrO3 Cathode Coating with Machine Learning Interatomic Potentials}},
author = {Ting Wang and Zihan Yu and Shyue Ping Ong},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15009236/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009236/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Design of High-Ionic-Conductivity Doped Li2ZrO3 Cathode Coating with Machine Learning Interatomic Potentials
AU - Ting Wang
AU - Zihan Yu
AU - Shyue Ping Ong
PY - 2026
DA - 2026/09/22
PB - ChemRxiv
DO - 10.26434/chemrxiv.15009236/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009236/v1
AB - Li2ZrO3 (LZO) is a promising cathode coating material for all-solid-state batteries, but its intrinsically low ionic conductivity limits rate performance. Here, we investigate the effects of Y3+, Al3+, Nb5+, and Ta5+ doping and amorphization on the Li+ conductivity of LZO using density functional theory (DFT) and machine learning interatomic potentials (MLIPs). Among the dopants studied, Y3+ substitution exhibits the most favorable defect formation energies. Molecular dynamics simulations using a fine-tuned moment tensor potential (MTP) reveal that 6%-Y-doped crystalline LZO (c-LYZO) achieves a maximum room-temperature ionic conductivity of ∼2mS cm−1, an order of magnitude improvement over undoped LZO. Amorphization was also found to enhance the conductivity of undoped LZO but the effect is attenuated in doped LZO. We show that amorphous LYZO exhibits more spatially fragmented, trapping-dominated Li transport than its crystalline counterpart. These results identify Y-doped LZO as a potential high-conductivity cathode coating and provide a design framework that balances dopant chemistry, defect type, doping concentration, and structural order.
ER -