Hierarchical high-throughput screening of alkaline-stable lithium-ion conductors combining machine learning and first-principles calculations
Zhuohan Li, KyuJung Jun, Bowen Deng, and Gerbrand Ceder

TL;DR
This paper presents a hierarchical high-throughput screening method combining machine learning and first-principles calculations to identify alkaline-stable lithium-ion conductors suitable for solid-state batteries in humid environments.
Contribution
It introduces a novel workflow that efficiently screens over 320,000 compositions, identifying 209 candidates with enhanced alkaline stability and elucidating stability mechanisms.
Findings
Identified 209 alkaline-stable lithium-ion conductors from 320,000+ candidates.
Revealed cation substitution strategies that improve stability in NASICON and garnet structures.
Highlighted trade-offs in composition optimization for stability, conductivity, and synthesizability.
Abstract
Solid-state batteries require lithium-ion conductors that combine high ionic conductivity with stability under harsh electrochemical and chemical conditions. Here, we investigate the chemical factors governing the stability of NASICON-type and garnet-type Li-ion conductors in highly alkaline environments. This is particularly relevant to solid-state Li-air cells operated under humidified air where alkaline conditions arise due to the formation of LiOH discharge products. We implement a hierarchical high-throughput screening workflow that consists of a pre-screening step using a universal machine-learning interatomic potential and a more accurate DFT-based screening. This approach enables rapid evaluation of over 320,000 compositions, from which 209 alkaline-stable candidates are identified. We identify specific cation substitutions that improve alkaline stability in NASICON and garnet…
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Taxonomy
TopicsMachine Learning in Materials Science · Advanced Battery Materials and Technologies · Advanced Battery Technologies Research
