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.
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Research Article|
August 01 2024
Machine learning approach accelerates search for solid state electrolytes
Special Collection:
Virtual issue on Machine Learning (2024)
Le Tang;
Le Tang
a
Department of Chemical Physics, University of Science and Technology of China
, Hefei 230026, China
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Guozhen Zhang;
Guozhen Zhang
b
Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China
, Hefei 230026, China
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Jun Jiang
Jun Jiang
*
a
Department of Chemical Physics, University of Science and Technology of China
, Hefei 230026, China
b
Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China
, Hefei 230026, China
*Author to whom correspondence should be addressed. E-mail: [email protected]
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Le Tang
1
Guozhen Zhang
2
Jun Jiang
1,2,*
a
Department of Chemical Physics, University of Science and Technology of China
, Hefei 230026, China
b
Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China
, Hefei 230026, China
*Author to whom correspondence should be addressed. E-mail: [email protected]
Chin. J. Chem. Phys. 37, 505–512 (2024)
Article history
Received:
February 18 2024
Accepted:
March 25 2024
Citation
Le Tang, Guozhen Zhang, Jun Jiang; Machine learning approach accelerates search for solid state electrolytes. Chin. J. Chem. Phys. 1 August 2024; 37 (4): 505–512. https://doi.org/10.1063/1674-0068/cjcp2402020
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