Harnessing explainable AI to adaptively design catalysts for lithium-sulfur batteries
The exploration of efficient catalysts for sluggish sulfur redox reactions is pivotal for advancing lithium-sulfur batteries but remains inefficient through trial-and-error approaches. In a recent Joule study, Zhou, Li, and colleagues proposed an explainable-AI-based approach to intelligently design...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Patterns |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666389925001047 |
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| _version_ | 1850190873786056704 |
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| author | Xinyan Liu Hong-Jie Peng |
| author_facet | Xinyan Liu Hong-Jie Peng |
| author_sort | Xinyan Liu |
| collection | DOAJ |
| description | The exploration of efficient catalysts for sluggish sulfur redox reactions is pivotal for advancing lithium-sulfur batteries but remains inefficient through trial-and-error approaches. In a recent Joule study, Zhou, Li, and colleagues proposed an explainable-AI-based approach to intelligently design catalysts adaptive to diverse local chemical environments in batteries, achieving exceptional catalytic and battery performance. |
| format | Article |
| id | doaj-art-40c494aee7e74061bd6f178df250abd3 |
| institution | OA Journals |
| issn | 2666-3899 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Patterns |
| spelling | doaj-art-40c494aee7e74061bd6f178df250abd32025-08-20T02:15:08ZengElsevierPatterns2666-38992025-05-016510125610.1016/j.patter.2025.101256Harnessing explainable AI to adaptively design catalysts for lithium-sulfur batteriesXinyan Liu0Hong-Jie Peng1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, P.R. ChinaInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; Corresponding authorThe exploration of efficient catalysts for sluggish sulfur redox reactions is pivotal for advancing lithium-sulfur batteries but remains inefficient through trial-and-error approaches. In a recent Joule study, Zhou, Li, and colleagues proposed an explainable-AI-based approach to intelligently design catalysts adaptive to diverse local chemical environments in batteries, achieving exceptional catalytic and battery performance.http://www.sciencedirect.com/science/article/pii/S2666389925001047 |
| spellingShingle | Xinyan Liu Hong-Jie Peng Harnessing explainable AI to adaptively design catalysts for lithium-sulfur batteries Patterns |
| title | Harnessing explainable AI to adaptively design catalysts for lithium-sulfur batteries |
| title_full | Harnessing explainable AI to adaptively design catalysts for lithium-sulfur batteries |
| title_fullStr | Harnessing explainable AI to adaptively design catalysts for lithium-sulfur batteries |
| title_full_unstemmed | Harnessing explainable AI to adaptively design catalysts for lithium-sulfur batteries |
| title_short | Harnessing explainable AI to adaptively design catalysts for lithium-sulfur batteries |
| title_sort | harnessing explainable ai to adaptively design catalysts for lithium sulfur batteries |
| url | http://www.sciencedirect.com/science/article/pii/S2666389925001047 |
| work_keys_str_mv | AT xinyanliu harnessingexplainableaitoadaptivelydesigncatalystsforlithiumsulfurbatteries AT hongjiepeng harnessingexplainableaitoadaptivelydesigncatalystsforlithiumsulfurbatteries |