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Machine Learning Guided Design of Doped Lithium Lanthanum Zirconium Oxide (LLZO) for Improved Performance
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DOI：10.1002/est2.70418.png)
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Abstract
En 中文
This paper presents a machine-learning approach to the development of all-solid-state batteries (ASSBs) with doped Lithium Lanthanum Zirconium Oxide (LLZO) electrolytes. LLZO electrolytes are promising solid electrolytes that exhibit high ionic conductivities of approximately ~10−3–10−4 S cm−1. However, optimizing the ionic conductivity of LLZO is a multiparametric challenge involving numerous compositional and processing variables. This work employs a data-driven methodology by applying machine learning to a dataset mined from the literature. This approach was used to model the intricate relationships between the compositional and synthesis variables and ionic conductivity. Tree-based and ensemble models, like Decision Tree, Random Forest, Light Gradient Boosting Machine, and CatBoost, are effective for interpreting complex relationships among variables. The models were trained with systematic hyperparameter tuning and rigorous cross-validation to predict ionic conductivity, achieving accuracies from 0.85 to 0.94. Feature importance and partial dependence analyses were used to quantify and visualize variable effects. Across the models, lithium composition and sintering conditions emerged as the most influential factors. Partial dependence plots were used to identify optimal synthesis and compositional ranges. By identifying promising search spaces, this approach can accelerate the design and discovery of high-performance LLZO electrolytes for solid-state batteries.
Keywords:
dopants
feature importance
lithium lanthanum zirconium oxide
machine learning
materials design
partial dependence plots
synthesis
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