Smart Materials Prediction: Applying Machine Learning to Lithium Solid-State Electrolyte.

Hu, Qianyu; Chen, Kunfeng; Liu, Fei; Zhao, Mengying; Liang, Feng; Xue, Dongfeng

Hu, Qianyu; Chen, Kunfeng; Liu, Fei; Zhao, Mengying; Liang, Feng; Xue, Dongfeng.

Afiliación

Hu Q; Institute of Novel Semiconductors, State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China.
Chen K; Institute of Novel Semiconductors, State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China.
Liu F; Wuhan Institute of Marine Electric Propulsion, CSIC, Wuhan 430064, China.
Zhao M; Institute of Novel Semiconductors, State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China.
Liang F; State Key Laboratory of Complex Non-Ferrous Metal Resources Clean Application, Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China.
Xue D; Multiscale Crystal Materials Research Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Materials (Basel) ; 15(3)2022 Feb 02.

Article en En | MEDLINE | ID: mdl-35161101

Palabras clave

lithium battery; machine learning; new materials discovery; solid state electrolyte

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Materials (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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