Data-driven design of electrolyte additives supporting high-performance 5 V LiNi0.5Mn1.5O4 positive electrodes
Abstract LiNi0.5Mn1.5O4 (LNMO) is a high-capacity spinel-structured material with an average lithiation/de-lithiation potential at ca. 4.6–4.7 V vs Li+/Li, far exceeding the stability limits of electrolytes. An efficient way to enable LNMO in lithium-ion batteries is to reformulate an electrolyte co...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-57961-w |
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| author | Bingning Wang Hieu A. Doan Seoung-Bum Son Daniel P. Abraham Stephen E. Trask Andrew Jansen Kang Xu Chen Liao |
| author_facet | Bingning Wang Hieu A. Doan Seoung-Bum Son Daniel P. Abraham Stephen E. Trask Andrew Jansen Kang Xu Chen Liao |
| author_sort | Bingning Wang |
| collection | DOAJ |
| description | Abstract LiNi0.5Mn1.5O4 (LNMO) is a high-capacity spinel-structured material with an average lithiation/de-lithiation potential at ca. 4.6–4.7 V vs Li+/Li, far exceeding the stability limits of electrolytes. An efficient way to enable LNMO in lithium-ion batteries is to reformulate an electrolyte composition that stabilizes both graphitic (Gr) negative electrode with solid-electrolyte-interphase and LNMO with cathode-electrolyte-interphase. In this study, we select and test a diverse collection of 28 single and dual additives for the Gr||LNMO battery system. Subsequently, we train machine learning models on this dataset and employ the trained models to suggest 6 binary compositions out of 125, based on predicted final area-specific-impedance, impedance rise, and final specific-capacity. Such machine learning-generated new additives outperform the initial dataset. This finding not only underscores the efficacy of machine learning in identifying materials in a highly complicated application space but also showcases an accelerated material discovery workflow that directly integrates data-driven methods with battery testing experiments. |
| format | Article |
| id | doaj-art-3ea13c104604432c82a6eb48ddfcaf0c |
| institution | DOAJ |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-3ea13c104604432c82a6eb48ddfcaf0c2025-08-20T03:06:52ZengNature PortfolioNature Communications2041-17232025-04-0116111010.1038/s41467-025-57961-wData-driven design of electrolyte additives supporting high-performance 5 V LiNi0.5Mn1.5O4 positive electrodesBingning Wang0Hieu A. Doan1Seoung-Bum Son2Daniel P. Abraham3Stephen E. Trask4Andrew Jansen5Kang Xu6Chen Liao7Chemical Sciences and Engineering Division, Argonne National LaboratoryMaterials Science Division, Argonne National LaboratoryChemical Sciences and Engineering Division, Argonne National LaboratoryChemical Sciences and Engineering Division, Argonne National LaboratoryChemical Sciences and Engineering Division, Argonne National LaboratoryChemical Sciences and Engineering Division, Argonne National LaboratorySES AI CorpsChemical Sciences and Engineering Division, Argonne National LaboratoryAbstract LiNi0.5Mn1.5O4 (LNMO) is a high-capacity spinel-structured material with an average lithiation/de-lithiation potential at ca. 4.6–4.7 V vs Li+/Li, far exceeding the stability limits of electrolytes. An efficient way to enable LNMO in lithium-ion batteries is to reformulate an electrolyte composition that stabilizes both graphitic (Gr) negative electrode with solid-electrolyte-interphase and LNMO with cathode-electrolyte-interphase. In this study, we select and test a diverse collection of 28 single and dual additives for the Gr||LNMO battery system. Subsequently, we train machine learning models on this dataset and employ the trained models to suggest 6 binary compositions out of 125, based on predicted final area-specific-impedance, impedance rise, and final specific-capacity. Such machine learning-generated new additives outperform the initial dataset. This finding not only underscores the efficacy of machine learning in identifying materials in a highly complicated application space but also showcases an accelerated material discovery workflow that directly integrates data-driven methods with battery testing experiments.https://doi.org/10.1038/s41467-025-57961-w |
| spellingShingle | Bingning Wang Hieu A. Doan Seoung-Bum Son Daniel P. Abraham Stephen E. Trask Andrew Jansen Kang Xu Chen Liao Data-driven design of electrolyte additives supporting high-performance 5 V LiNi0.5Mn1.5O4 positive electrodes Nature Communications |
| title | Data-driven design of electrolyte additives supporting high-performance 5 V LiNi0.5Mn1.5O4 positive electrodes |
| title_full | Data-driven design of electrolyte additives supporting high-performance 5 V LiNi0.5Mn1.5O4 positive electrodes |
| title_fullStr | Data-driven design of electrolyte additives supporting high-performance 5 V LiNi0.5Mn1.5O4 positive electrodes |
| title_full_unstemmed | Data-driven design of electrolyte additives supporting high-performance 5 V LiNi0.5Mn1.5O4 positive electrodes |
| title_short | Data-driven design of electrolyte additives supporting high-performance 5 V LiNi0.5Mn1.5O4 positive electrodes |
| title_sort | data driven design of electrolyte additives supporting high performance 5 v lini0 5mn1 5o4 positive electrodes |
| url | https://doi.org/10.1038/s41467-025-57961-w |
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