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Data-Driven Composition-Only Machine Learning for High-Performance Solid-State Electrolytes
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DOI：10.1039/D5QM00438A.png)
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Abstract
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As a pivotal advancement in energy storage technology; all-solid-state batteries represent a transformative direction for next-generation lithium-ion batteries. To address the critical challenge of low ionic conductivity in solid-state electrolytes (SSEs); we propose a machine learning driven screening workflow to search for SSE with high ionic conductivity. Leveraging an experimentally database of lithium-ion SSEs; we train five ensemble boosting models using exclusively elemental composition and temperature parameters. The CatBoost algorithm emerges as the optimal predictor; achieving superior accuracy in ionic conductivity estimation. Implementing this model; we systematically screen 3; 311 lithium-containing materials from the Materials Project database; identifying 22 promising candidates with predicted ionic conductivity exceeding 1 mS/cm. Especially; the predicted conductivity of Li8SeN2 (2.72 mS/cm) is well consistent with the AIMD measurement (2.85 mS/cm). This data-driven approach accelerates SSE discovery while providing fundamental insights into structure-property relationships; establishing a robust framework for next-generation electrolyte development.
Keywords:
all-solid-state batteries
solid-state electrolytes
ionic conductivity
machine learning
materials screening
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6.4
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3.2K
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1.5W
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