Artificial intelligence for the understanding of electrolyte chemistry and electrode interface in lithium battery
Recognizing the critical role of electrolyte chemistry and electrode interfaces in the performance and safety of lithium batteries, along with the urgent need for more sophisticated methods of analysis, this comprehensive review underscores the promise of machine learning (ML) models in this researc...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Science Press
2023-12-01
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| Series: | National Science Open |
| Subjects: | |
| Online Access: | https://www.sciengine.com/doi/10.1360/nso/20230039 |
| _version_ | 1797205959045545984 |
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| author | Chen Yawei Liu Yue He Zixu Xu Liang Yu Peiping Sun Qintao Li Wanxia Jie Yulin Cao Ruiguo Cheng Tao Jiao Shuhong |
| author_facet | Chen Yawei Liu Yue He Zixu Xu Liang Yu Peiping Sun Qintao Li Wanxia Jie Yulin Cao Ruiguo Cheng Tao Jiao Shuhong |
| author_sort | Chen Yawei |
| collection | DOAJ |
| description | Recognizing the critical role of electrolyte chemistry and electrode interfaces in the performance and safety of lithium batteries, along with the urgent need for more sophisticated methods of analysis, this comprehensive review underscores the promise of machine learning (ML) models in this research field. It explores the application of these innovative methods to studying battery interfaces, particularly focusing on lithium metal anodes. Amid the limitations of traditional experimental techniques, the review supports a hybrid approach that couples experimental and simulation methods, enabling granular insights into the formation process and characteristics of battery interfaces at the molecular level and harnessing AI to extract patterns from voluminous data sets. It showcases the utility of such techniques in electrolyte design and battery life prediction and introduces a novel perspective on battery interface mechanisms. The review concludes by asserting the potential of artificial intelligence (AI) or ML models as invaluable tools in the future of battery research and highlights the importance of fostering confidence in these technologies within the scientific community. |
| first_indexed | 2024-04-24T08:59:24Z |
| format | Article |
| id | doaj.art-2a03d7b33da3431d9bc227dd1d168163 |
| institution | Directory Open Access Journal |
| issn | 2097-1168 |
| language | English |
| last_indexed | 2024-04-24T08:59:24Z |
| publishDate | 2023-12-01 |
| publisher | Science Press |
| record_format | Article |
| series | National Science Open |
| spelling | doaj.art-2a03d7b33da3431d9bc227dd1d1681632024-04-16T03:19:38ZengScience PressNational Science Open2097-11682023-12-01310.1360/nso/20230039eb33e642Artificial intelligence for the understanding of electrolyte chemistry and electrode interface in lithium batteryChen Yawei0Liu Yue1He Zixu2Xu Liang3Yu Peiping4Sun Qintao5Li Wanxia6Jie Yulin7Cao Ruiguo8Cheng Tao9Jiao Shuhong10["Hefei National Laboratory for Physical Science at Microscale, CAS Key Laboratory of Materials for Energy Conversion, Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China"]["Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, China"]["Hefei National Laboratory for Physical Science at Microscale, CAS Key Laboratory of Materials for Energy Conversion, Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China"]["Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, China"]["Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, China"]["Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, China"]["Hefei National Laboratory for Physical Science at Microscale, CAS Key Laboratory of Materials for Energy Conversion, Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China"]["Hefei National Laboratory for Physical Science at Microscale, CAS Key Laboratory of Materials for Energy Conversion, Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China"]["Hefei National Laboratory for Physical Science at Microscale, CAS Key Laboratory of Materials for Energy Conversion, Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China"]["Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, China","Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou 215123, China"]["Hefei National Laboratory for Physical Science at Microscale, CAS Key Laboratory of Materials for Energy Conversion, Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China"]Recognizing the critical role of electrolyte chemistry and electrode interfaces in the performance and safety of lithium batteries, along with the urgent need for more sophisticated methods of analysis, this comprehensive review underscores the promise of machine learning (ML) models in this research field. It explores the application of these innovative methods to studying battery interfaces, particularly focusing on lithium metal anodes. Amid the limitations of traditional experimental techniques, the review supports a hybrid approach that couples experimental and simulation methods, enabling granular insights into the formation process and characteristics of battery interfaces at the molecular level and harnessing AI to extract patterns from voluminous data sets. It showcases the utility of such techniques in electrolyte design and battery life prediction and introduces a novel perspective on battery interface mechanisms. The review concludes by asserting the potential of artificial intelligence (AI) or ML models as invaluable tools in the future of battery research and highlights the importance of fostering confidence in these technologies within the scientific community.https://www.sciengine.com/doi/10.1360/nso/20230039lithium batteriesbattery interfacesartificial intelligencemachine learningelectrolyte chemistry |
| spellingShingle | Chen Yawei Liu Yue He Zixu Xu Liang Yu Peiping Sun Qintao Li Wanxia Jie Yulin Cao Ruiguo Cheng Tao Jiao Shuhong Artificial intelligence for the understanding of electrolyte chemistry and electrode interface in lithium battery National Science Open lithium batteries battery interfaces artificial intelligence machine learning electrolyte chemistry |
| title | Artificial intelligence for the understanding of electrolyte chemistry and electrode interface in lithium battery |
| title_full | Artificial intelligence for the understanding of electrolyte chemistry and electrode interface in lithium battery |
| title_fullStr | Artificial intelligence for the understanding of electrolyte chemistry and electrode interface in lithium battery |
| title_full_unstemmed | Artificial intelligence for the understanding of electrolyte chemistry and electrode interface in lithium battery |
| title_short | Artificial intelligence for the understanding of electrolyte chemistry and electrode interface in lithium battery |
| title_sort | artificial intelligence for the understanding of electrolyte chemistry and electrode interface in lithium battery |
| topic | lithium batteries battery interfaces artificial intelligence machine learning electrolyte chemistry |
| url | https://www.sciengine.com/doi/10.1360/nso/20230039 |
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