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Machine learning approach accelerates search for solid state electrolytes.
- Source :
- Chinese Journal of Chemical Physics (1674-0068); Aug2024, Vol. 37 Issue 4, p505-512, 8p
- Publication Year :
- 2024
-
Abstract
- In the current aera of rapid development in the field of electric vehicles and electrochemical energy storage, solid-state battery technology is attracting much research and attention. Solid-state electrolytes, as the key component of next-generation battery technology, are favored for their high safety, high energy density, and long life. However, finding high-performance solid-state electrolytes is the primary challenge for solid-state battery applications. Focusing on inorganic solid-state electrolytes, this work highlights the need for ideal solid-state electrolytes to have low electronic conductivity, good thermal stability, and structural and phase stability. Traditional experimental and theoretical computational methods suffer from inefficiency, thus machine learning methods become a novel path to intelligently predict material properties by analyzing a large number of inorganic structural properties and characteristics. Through the gradient descent-based XGBoost algorithm, we successfully predicted the energy band structure and stability of the materials, and screened out only 194 ideal solid-state electrolyte structures from more than 6000 structures that satisfy the requirements of low electronic conductivity and stability simultaneously, which greatly accelerated the development of solid-state batteries. [ABSTRACT FROM AUTHOR]
- Subjects :
- SOLID electrolytes
ENERGY density
ELECTRIC fields
ENERGY storage
ENERGY bands
Subjects
Details
- Language :
- English
- ISSN :
- 16740068
- Volume :
- 37
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Chinese Journal of Chemical Physics (1674-0068)
- Publication Type :
- Academic Journal
- Accession number :
- 178838092
- Full Text :
- https://doi.org/10.1063/1674-0068/cjcp2402020