Evidence map›Paper›PMID 40245341›Full record

ArticleAngewandte Chemie (International ed. in English)2025

Unraveling the Complexity of Divalent Hydride Electrolytes in Solid-State Batteries via a Data-Driven Framework with Large Language Model.

Qian Wang, Fangling Yang, Yuhang Wang, Di Zhang, Ryuhei Sato, Linda Zhang, Eric Jianfeng Cheng, Yigang Yan, Yungui Chen, Kazuaki Kisu and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Angewandte Chemie (International ed. in English), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Facile Synthesis of Inorganic LiAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Qian WangAdvanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, 980-8577, Japan.
Fangling YangAdvanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, 980-8577, Japan.
Yuhang WangAdvanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, 980-8577, Japan.
Di ZhangAdvanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, 980-8577, Japan.
Ryuhei SatoDepartment of Materials Engineering, The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo, 113-8656, Japan.
Linda ZhangAdvanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, 980-8577, Japan.
Eric Jianfeng ChengAdvanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, 980-8577, Japan.
Yigang YanInstitute of New Energy and Low-Carbon Technology, Sichuan University, Chengdu, 610207, China.
Yungui ChenInstitute of New Energy and Low-Carbon Technology, Sichuan University, Chengdu, 610207, China.
Kazuaki KisuCollege of Engineering, Shibaura Institute of Technology, Tokyo, 135-8548, Japan.
Shin-Ichi OrimoAdvanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, 980-8577, Japan.
Hao LiAdvanced Institute for Materials Research (WPI-AIMR), Tohoku University, Sendai, 980-8577, Japan.

Funding

JSPS KAKENHI JP23K13542JSPS KAKENHI JP23K13703JSPS KAKENHI JP24K23068
6 · The paper itself

Abstract

Solid-state electrolytes (SSEs) are essential for next-generation energy storage technologies. However, the exploration of divalent hydrides is hindered by complex ionic migration mechanisms and reliance on "trial-and-error" methodologies. Conventional approaches, which focus on individual materials and predefined pathways, remain inefficient. Herein, we present a data-driven artificial intelligence framework that integrates a comprehensive SSE database with large language models and ab initio metadynamics (MetaD) simulations to accelerate the discovery of hydride SSEs. Our study reveals that hydrides incorporating neutral molecules have great potential, with MetaD revealing novel "two-step" ion migration mechanisms. Predictive models developed using both experimental and computational data accurately forecast ionic migration activation energies for various types of hydride SSEs. In particular, some SSEs with carbon-containing neutral molecules exhibit notably low activation energy, with barriers as low as 0.62 eV. This framework enables the rapid identification of optimized SSE candidates and establishes a transformative tool for advancing sustainable energy storage technologies.

Indexed as

AI for materialsBig data analyticsData‐driven frameworkHydride materialsSolid‐state electrolyte

Identifiers

PMID40245341
PMCPMC12171339

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.