Artificial Intelligence Empowers Solid-State Batteries for Material Screening and Performance Evaluation
Highlights The latest advancements in the application of machine learning (ML) for the screening of solid-state battery materials are reviewed. The achievements of various ML algorithms in predicting different performances of the battery management system are discussed. Future challenges and perspec...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-06-01
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| Series: | Nano-Micro Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40820-025-01797-y |
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| _version_ | 1849235024064282624 |
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| author | Sheng Wang Jincheng Liu Xiaopan Song Huajian Xu Yang Gu Junyu Fan Bin Sun Linwei Yu |
| author_facet | Sheng Wang Jincheng Liu Xiaopan Song Huajian Xu Yang Gu Junyu Fan Bin Sun Linwei Yu |
| author_sort | Sheng Wang |
| collection | DOAJ |
| description | Highlights The latest advancements in the application of machine learning (ML) for the screening of solid-state battery materials are reviewed. The achievements of various ML algorithms in predicting different performances of the battery management system are discussed. Future challenges and perspectives of artificial intelligence in solid-state battery are discussed. |
| format | Article |
| id | doaj-art-cb0e18aeeb97444ca2cba15e9fbcccb8 |
| institution | Kabale University |
| issn | 2311-6706 2150-5551 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Nano-Micro Letters |
| spelling | doaj-art-cb0e18aeeb97444ca2cba15e9fbcccb82025-08-20T04:02:55ZengSpringerOpenNano-Micro Letters2311-67062150-55512025-06-0117113110.1007/s40820-025-01797-yArtificial Intelligence Empowers Solid-State Batteries for Material Screening and Performance EvaluationSheng Wang0Jincheng Liu1Xiaopan Song2Huajian Xu3Yang Gu4Junyu Fan5Bin Sun6Linwei Yu7School of Future Science and Engineering, Soochow UniversitySchool of Future Science and Engineering, Soochow UniversitySchool of Electronics Science and Engineering, Nanjing UniversitySchool of Electronics Science and Engineering, Soochow UniversitySchool of Future Science and Engineering, Soochow UniversitySchool of Future Science and Engineering, Soochow UniversitySchool of Future Science and Engineering, Soochow UniversitySchool of Electronics Science and Engineering, Nanjing UniversityHighlights The latest advancements in the application of machine learning (ML) for the screening of solid-state battery materials are reviewed. The achievements of various ML algorithms in predicting different performances of the battery management system are discussed. Future challenges and perspectives of artificial intelligence in solid-state battery are discussed.https://doi.org/10.1007/s40820-025-01797-ySolid-state batteriesArtificial intelligenceDeep learningMaterial screeningPerformance evaluation |
| spellingShingle | Sheng Wang Jincheng Liu Xiaopan Song Huajian Xu Yang Gu Junyu Fan Bin Sun Linwei Yu Artificial Intelligence Empowers Solid-State Batteries for Material Screening and Performance Evaluation Nano-Micro Letters Solid-state batteries Artificial intelligence Deep learning Material screening Performance evaluation |
| title | Artificial Intelligence Empowers Solid-State Batteries for Material Screening and Performance Evaluation |
| title_full | Artificial Intelligence Empowers Solid-State Batteries for Material Screening and Performance Evaluation |
| title_fullStr | Artificial Intelligence Empowers Solid-State Batteries for Material Screening and Performance Evaluation |
| title_full_unstemmed | Artificial Intelligence Empowers Solid-State Batteries for Material Screening and Performance Evaluation |
| title_short | Artificial Intelligence Empowers Solid-State Batteries for Material Screening and Performance Evaluation |
| title_sort | artificial intelligence empowers solid state batteries for material screening and performance evaluation |
| topic | Solid-state batteries Artificial intelligence Deep learning Material screening Performance evaluation |
| url | https://doi.org/10.1007/s40820-025-01797-y |
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