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. Int. Ed. 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.
Authors
- Yigang Yan (ORCID: https://orcid.org/0000-0002-4910-1465)
- Kazuaki Kisu (ORCID: https://orcid.org/0000-0002-1693-4225)
- Fangling Yang (ORCID: https://orcid.org/0009-0000-2938-9337)
- Linda Zhang (ORCID: https://orcid.org/0000-0003-3841-544X)
- Yungui Chen (ORCID: https://orcid.org/0000-0001-8403-7289)
- Shin‐ichi Orimo (ORCID: https://orcid.org/0000-0002-4216-0446)
- Eric Jianfeng Cheng (ORCID: https://orcid.org/0000-0003-0904-802X)
- Hao Li (ORCID: https://orcid.org/0000-0002-7577-1366)
- Di Zhang (ORCID: https://orcid.org/0000-0002-9129-1063)
- Qian Wang (ORCID: https://orcid.org/0000-0002-3490-3836)
- Yuhang Wang (ORCID: https://orcid.org/0009-0003-8376-0055)
- Ryuhei Sato (ORCID: https://orcid.org/0000-0002-5503-0982)
Institutions
- Shibaura Institute of Technology (JP)
- Tohoku University (JP)
- Sichuan University (CN)
- Advanced Institute of Materials Science (JP)
- The University of Tokyo (JP)
Publication Details
- Journal
- Angewandte Chemie International Edition
- Published
- 2025-05-28
- DOI
- https://doi.org/10.1002/anie.202510922
- Primary Topic
- Fuel Cells and Related Materials
- Type
- article
- Field-Weighted Citation Impact
- 0.00