Machine-learning-aided screening of inorganic lithium solid-state electrolytes with a wide electrochemical window

Jiajing Chen; Lu Jiang; Shendong Tan; Jun Yang; Zihui Li; Chen Bai; Xiang Zhang; Rongao Li; Yaoshu Xie; Ming Liu; Yan Bing He; Tingzheng Hou

doi:10.1039/d5ta04197j

Machine-learning-aided screening of inorganic lithium solid-state electrolytes with a wide electrochemical window

Jiajing Chen
, Lu Jiang
, Shendong Tan
, Jun Yang
, Zihui Li
, Chen Bai
, Xiang Zhang
, Rongao Li
, Yaoshu Xie
, Ming Liu
, Yan Bing He
, Tingzheng Hou^*

^*Corresponding author for this work

Department of Materials Science and Engineering

Tsinghua University

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

We construct an electrochemical window (ECW) dataset of over 16 000 Li-containing compounds using a thermodynamic approach for solid-state electrolytes (SSEs). A data-driven ECW prediction framework is developed, with the classification model achieving >0.98 accuracy and the regression model yielding mean absolute errors of 0.19/0.21 V for the left/right ECW limits. Screening 69 243 compounds identifies promising SSE candidates, enabling accelerated discovery of electrochemically stable materials.

Original language	English
Pages (from-to)	23445-23453
Number of pages	9
Journal	Journal of Materials Chemistry A
Volume	13
Issue number	29
DOIs	https://doi.org/10.1039/d5ta04197j
State	Published - 22 Jul 2025

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title = "Machine-learning-aided screening of inorganic lithium solid-state electrolytes with a wide electrochemical window",

abstract = "We construct an electrochemical window (ECW) dataset of over 16 000 Li-containing compounds using a thermodynamic approach for solid-state electrolytes (SSEs). A data-driven ECW prediction framework is developed, with the classification model achieving >0.98 accuracy and the regression model yielding mean absolute errors of 0.19/0.21 V for the left/right ECW limits. Screening 69 243 compounds identifies promising SSE candidates, enabling accelerated discovery of electrochemically stable materials.",

author = "Jiajing Chen and Lu Jiang and Shendong Tan and Jun Yang and Zihui Li and Chen Bai and Xiang Zhang and Rongao Li and Yaoshu Xie and Ming Liu and He, \{Yan Bing\} and Tingzheng Hou",

note = "Publisher Copyright: {\textcopyright} 2025 The Royal Society of Chemistry.",

year = "2025",

month = jul,

day = "22",

doi = "10.1039/d5ta04197j",

language = "英语",

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pages = "23445--23453",

journal = "Journal of Materials Chemistry A",

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TY - JOUR

T1 - Machine-learning-aided screening of inorganic lithium solid-state electrolytes with a wide electrochemical window

AU - Chen, Jiajing

AU - Jiang, Lu

AU - Tan, Shendong

AU - Yang, Jun

AU - Li, Zihui

AU - Bai, Chen

AU - Zhang, Xiang

AU - Li, Rongao

AU - Xie, Yaoshu

AU - Liu, Ming

AU - He, Yan Bing

AU - Hou, Tingzheng

PY - 2025/7/22

Y1 - 2025/7/22

N2 - We construct an electrochemical window (ECW) dataset of over 16 000 Li-containing compounds using a thermodynamic approach for solid-state electrolytes (SSEs). A data-driven ECW prediction framework is developed, with the classification model achieving >0.98 accuracy and the regression model yielding mean absolute errors of 0.19/0.21 V for the left/right ECW limits. Screening 69 243 compounds identifies promising SSE candidates, enabling accelerated discovery of electrochemically stable materials.

AB - We construct an electrochemical window (ECW) dataset of over 16 000 Li-containing compounds using a thermodynamic approach for solid-state electrolytes (SSEs). A data-driven ECW prediction framework is developed, with the classification model achieving >0.98 accuracy and the regression model yielding mean absolute errors of 0.19/0.21 V for the left/right ECW limits. Screening 69 243 compounds identifies promising SSE candidates, enabling accelerated discovery of electrochemically stable materials.

UR - https://www.scopus.com/pages/publications/105010230604

U2 - 10.1039/d5ta04197j

DO - 10.1039/d5ta04197j

M3 - 文章

AN - SCOPUS:105010230604

SN - 2050-7488

VL - 13

SP - 23445

EP - 23453

JO - Journal of Materials Chemistry A

JF - Journal of Materials Chemistry A

IS - 29

ER -

Machine-learning-aided screening of inorganic lithium solid-state electrolytes with a wide electrochemical window

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