外封面:解开二价氢化物电解质的复杂性在固态电池通过一个数据驱动的框架与大语言模型。化学25/2025)

Qian Wang, Fangling Yang, Yuhang Wang, Di Zhang, Ryuhei Sato, Linda Zhang, Eric Jianfeng Cheng, Yigang Yan, Yungui Chen, Kazuaki Kisu, Shin-ichi Orimo, Hao Li
{"title":"外封面:解开二价氢化物电解质的复杂性在固态电池通过一个数据驱动的框架与大语言模型。化学25/2025)","authors":"Qian Wang,&nbsp;Fangling Yang,&nbsp;Yuhang Wang,&nbsp;Di Zhang,&nbsp;Ryuhei Sato,&nbsp;Linda Zhang,&nbsp;Eric Jianfeng Cheng,&nbsp;Yigang Yan,&nbsp;Yungui Chen,&nbsp;Kazuaki Kisu,&nbsp;Shin-ichi Orimo,&nbsp;Hao Li","doi":"10.1002/ange.202510922","DOIUrl":null,"url":null,"abstract":"<p>By integrating large language models, big data analytics, and ab initio metadynamics simulations, Hao Li and co-workers developed a powerful AI-driven framework that unravels previously hidden ion migration pathways in hydride-based solid-state electrolytes (SSEs) (e202506573). This integrative approach enables accurate prediction and efficient screening of high-performance SSEs, paving the way for next-generation multivalent solid-state batteries.\n\n <figure>\n <div><picture>\n <source></source></picture><p></p>\n </div>\n </figure></p>","PeriodicalId":7803,"journal":{"name":"Angewandte Chemie","volume":"137 25","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ange.202510922","citationCount":"0","resultStr":"{\"title\":\"Outside Front Cover: Unraveling the Complexity of Divalent Hydride Electrolytes in Solid-State Batteries via a Data-Driven Framework with Large Language Model (Angew. Chem. 25/2025)\",\"authors\":\"Qian Wang,&nbsp;Fangling Yang,&nbsp;Yuhang Wang,&nbsp;Di Zhang,&nbsp;Ryuhei Sato,&nbsp;Linda Zhang,&nbsp;Eric Jianfeng Cheng,&nbsp;Yigang Yan,&nbsp;Yungui Chen,&nbsp;Kazuaki Kisu,&nbsp;Shin-ichi Orimo,&nbsp;Hao Li\",\"doi\":\"10.1002/ange.202510922\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>By integrating large language models, big data analytics, and ab initio metadynamics simulations, Hao Li and co-workers developed a powerful AI-driven framework that unravels previously hidden ion migration pathways in hydride-based solid-state electrolytes (SSEs) (e202506573). This integrative approach enables accurate prediction and efficient screening of high-performance SSEs, paving the way for next-generation multivalent solid-state batteries.\\n\\n <figure>\\n <div><picture>\\n <source></source></picture><p></p>\\n </div>\\n </figure></p>\",\"PeriodicalId\":7803,\"journal\":{\"name\":\"Angewandte Chemie\",\"volume\":\"137 25\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ange.202510922\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Angewandte Chemie\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/ange.202510922\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Angewandte Chemie","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ange.202510922","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

通过集成大型语言模型、大数据分析和从头算元动力学模拟,李浩及其同事开发了一个强大的人工智能驱动框架,揭示了以前隐藏在氢化物固态电解质(sse)中的离子迁移途径(e202506573)。这种综合方法可以准确预测和有效筛选高性能sse,为下一代多价固态电池铺平道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Outside Front Cover: Unraveling the Complexity of Divalent Hydride Electrolytes in Solid-State Batteries via a Data-Driven Framework with Large Language Model (Angew. Chem. 25/2025)

Outside Front Cover: Unraveling the Complexity of Divalent Hydride Electrolytes in Solid-State Batteries via a Data-Driven Framework with Large Language Model (Angew. Chem. 25/2025)

By integrating large language models, big data analytics, and ab initio metadynamics simulations, Hao Li and co-workers developed a powerful AI-driven framework that unravels previously hidden ion migration pathways in hydride-based solid-state electrolytes (SSEs) (e202506573). This integrative approach enables accurate prediction and efficient screening of high-performance SSEs, paving the way for next-generation multivalent solid-state batteries.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Angewandte Chemie
Angewandte Chemie 化学科学, 有机化学, 有机合成
自引率
0.00%
发文量
0
审稿时长
1 months
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信