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Improving ionic conductivity of garnet solid-state electrolytes using Gradient boosting regression optimized machine learning
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AI
DOI：10.1016/j.jpowsour.2024.234492.png)
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Abstract
En 中文
Garnet solid-state electrolytes have become one of the most promising electrolyte materials due to their high ionic conductivity, wide electrochemical window, and excellent electrochemical stability. However, the trialand -error method used to screen high-performance garnet solid-state electrolytes has the disadvantages of a long development cycle and high cost. Machine learning methods based on big data can be independent of the physical mechanism of the material, and improved the efficiency of material development. In this work, the effect of structural factor (t), first ionization energy, and other feature descriptors on ionic conductivity were studied by using the Gradient boosting regression (GBR), Random Forest (RF), eXtreme Gradient Boosting (XGB), and other models. Machine learning models can improve the accuracy of predicting the ionic conductivity of garnet solid-state electrolytes, and have guided the preparation of five garnet -type solid electrolyte materials, the ionic conductivity of Li 6 & sdot;2 La 3 Zr 2 Fe 0 & sdot;25 O 12 is 1.08 x 10 -4 S cm - 1 , while the predicted value is 1.37 x 10 -4 S cm - 1 , the descriptors in the model will provide a reference for other researchers.
Keywords:
Solid-state batteries
Ionic conductivity
Machine learning
Garnet electrolytes
GBR optimization
Journal
IF:
7.9
Papers:
3.7W
Citations:
15.0W
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