Machine-Learning Approaches for the Discovery of Electrolyte Materials for Solid-State Lithium Batteries
Solid-state lithium batteries have attracted considerable research attention for their potential advantages over conventional liquid electrolyte lithium batteries. The discovery of lithium solid-state electrolytes (SSEs) is still undergoing to solve the remaining challenges, and machine learning (ML...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Batteries |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-0105/9/4/228 |
| _version_ | 1797606367230427136 |
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| author | Shengyi Hu Chun Huang |
| author_facet | Shengyi Hu Chun Huang |
| author_sort | Shengyi Hu |
| collection | DOAJ |
| description | Solid-state lithium batteries have attracted considerable research attention for their potential advantages over conventional liquid electrolyte lithium batteries. The discovery of lithium solid-state electrolytes (SSEs) is still undergoing to solve the remaining challenges, and machine learning (ML) approaches could potentially accelerate the process significantly. This review introduces common ML techniques employed in materials discovery and an overview of ML applications in lithium SSE discovery, with perspectives on the key issues and future outlooks. |
| first_indexed | 2024-03-11T05:14:12Z |
| format | Article |
| id | doaj.art-970354c77e664413aa3ca2c52a79a82c |
| institution | Directory Open Access Journal |
| issn | 2313-0105 |
| language | English |
| last_indexed | 2024-03-11T05:14:12Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj.art-970354c77e664413aa3ca2c52a79a82c2023-11-17T18:20:25ZengMDPI AGBatteries2313-01052023-04-019422810.3390/batteries9040228Machine-Learning Approaches for the Discovery of Electrolyte Materials for Solid-State Lithium BatteriesShengyi Hu0Chun Huang1Department of Materials, Imperial College London, London SW7 2AZ, UKDepartment of Materials, Imperial College London, London SW7 2AZ, UKSolid-state lithium batteries have attracted considerable research attention for their potential advantages over conventional liquid electrolyte lithium batteries. The discovery of lithium solid-state electrolytes (SSEs) is still undergoing to solve the remaining challenges, and machine learning (ML) approaches could potentially accelerate the process significantly. This review introduces common ML techniques employed in materials discovery and an overview of ML applications in lithium SSE discovery, with perspectives on the key issues and future outlooks.https://www.mdpi.com/2313-0105/9/4/228solid-state batteriesmachine learningsolid-state electrolytematerials discovery |
| spellingShingle | Shengyi Hu Chun Huang Machine-Learning Approaches for the Discovery of Electrolyte Materials for Solid-State Lithium Batteries Batteries solid-state batteries machine learning solid-state electrolyte materials discovery |
| title | Machine-Learning Approaches for the Discovery of Electrolyte Materials for Solid-State Lithium Batteries |
| title_full | Machine-Learning Approaches for the Discovery of Electrolyte Materials for Solid-State Lithium Batteries |
| title_fullStr | Machine-Learning Approaches for the Discovery of Electrolyte Materials for Solid-State Lithium Batteries |
| title_full_unstemmed | Machine-Learning Approaches for the Discovery of Electrolyte Materials for Solid-State Lithium Batteries |
| title_short | Machine-Learning Approaches for the Discovery of Electrolyte Materials for Solid-State Lithium Batteries |
| title_sort | machine learning approaches for the discovery of electrolyte materials for solid state lithium batteries |
| topic | solid-state batteries machine learning solid-state electrolyte materials discovery |
| url | https://www.mdpi.com/2313-0105/9/4/228 |
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