دورية أكاديمية
Attention-based solubility prediction of polysulfide and electrolyte analysis for lithium–sulfur batteries
| العنوان: | Attention-based solubility prediction of polysulfide and electrolyte analysis for lithium–sulfur batteries |
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| المؤلفون: | Jaewan Lee, Hongjun Yang, Changyoung Park, Seong-Hyo Park, Eunji Jang, Hobeom Kwack, Chang Hoon Lee, Chang-ik Song, Young Cheol Choi, Sehui Han, Honglak Lee |
| المصدر: | Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023) |
| بيانات النشر: | Nature Portfolio, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | Abstract During the continuous charge and discharge process in lithium-sulfur batteries, one of the next-generation batteries, polysulfides are generated in the battery’s electrolyte, and impact its performance in terms of power and capacity by involving the process. The amount of polysulfides in the electrolyte could be estimated by the change of the Gibbs free energy of the electrolyte, $$\Delta _{mix}\textrm{G}$$ Δ mix G in the presence of polysulfide. However, obtaining $$\Delta _{mix}\textrm{G}$$ Δ mix G of the diverse mixtures of components in the electrolyte is a complex and expensive task that shows itself as a bottleneck in optimization of electrolytes. In this work, we present a machine-learning approach for predicting $$\Delta _{mix}\textrm{G}$$ Δ mix G of electrolytes. The proposed architecture utilizes (1) an attention-based model (Attentive FP), a contrastive learning model (MolCLR) or morgan fingerprints to represent chemical components, and (2) transformers to account for the interactions between chemicals in the electrolyte. This architecture was not only capable of predicting electrolyte properties, including those of chemicals not used during training, but also providing insights into chemical interactions within electrolytes. It revealed that interactions with other chemicals relate to the logP and molecular weight of the chemicals. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-023-47154-0 |
| URL الوصول: | https://doaj.org/article/ce5cdee094f54dd6b70e68d4fc0260e9 |
| رقم الأكسشن: | edsdoj.5cdee094f54dd6b70e68d4fc0260e9 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-023-47154-0 |