Predicting Li-Ion Migration Energy Barriers in Battery Cathode Materials via Convolutional Neural Network Model Based on Descriptors Divide-and-Conquer Strategy

, and H0 bars suggest a synergistic effect of multiple structural descriptors, highlighting the necessity of adopting hybrid geometric-topological descriptors. The present work provides an efficient and accurate approach for high-throughput screening of materials with rapid Li-ion diffusion.

Authors

Siqi Shi (ORCID: https://orcid.org/0000-0001-8988-9763)
X Liu
Yi Jin (ORCID: https://orcid.org/0000-0001-6203-1281)
T Liu
Fanghua Ning (ORCID: https://orcid.org/0000-0001-6590-958X)
Shigang Lu
Zhiyan Xue
Yan Tian
Jingwen Dai

Institutions

Shanghai University (CN)
Centre for Sustainable Energy (GB)

Publication Details

Journal: The Journal of Physical Chemistry Letters
Published: 2026-06-18
DOI: https://doi.org/10.1021/acs.jpclett.6c01620
Primary Topic: Machine Learning in Materials Science
Type: article
Field-Weighted Citation Impact: 0.00

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article

Predicting Li-Ion Migration Energy Barriers in Battery Cathode Materials via Convolutional Neural Network Model Based on Descriptors Divide-and-Conquer Strategy

Siqi Shi, X Liu, Yi Jin, T Liu et al.

Jun 18, 2026

The Journal of Physical Chemistry Letters

Machine Learning in Materials Science

article

Predicting Li-Ion Migration Energy Barriers in Battery Cathode Materials via Convolutional Neural Network Model Based on Descriptors Divide-and-Conquer Strategy

Siqi Shi, X Liu, Yi Jin, T Liu, Fanghua Ning, Shigang Lu, Zhiyan Xue, Yan Tian, Jingwen Dai

June 18, 2026article en

Abstract

, and H0 bars suggest a synergistic effect of multiple structural descriptors, highlighting the necessity of adopting hybrid geometric-topological descriptors. The present work provides an efficient and accurate approach for high-throughput screening of materials with rapid Li-ion diffusion.

The Journal of Physical Chemistry Letters

Shanghai University (CN), Centre for Sustainable Energy (GB)

Affordable and clean energy

Openalex Percentile: Top 13%

Machine Learning in Materials Science

DOI Source OpenAlex

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