Work

Design of High-Ionic-Conductivity Doped Li2ZrO3 Cathode Coating with Machine Learning Interatomic Potentials

Year: 2026

Type: preprint

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.

Source: ChemRxiv

DOI: 10.26434/chemrxiv.15009236/v1

Authors: Ting Wang, Zihan Yu, Shyue Ping Ong

Institutions: University of California San Diego, National University of Singapore

Language: English

Topic: Advanced Battery Materials and Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: green

Funders: Nissan North America, San Diego Supercomputer Center, National University of Singapore, University of California, San Diego

OpenAlex ID: W7213983987

Cites: 3

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Related to: 10