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

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

doi:10.1021/acs.jpclett.6c01620

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

The Journal of Physical Chemistry Letters 2026

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

DOI: 10.1021/acs.jpclett.6c01620

Cited by: 1

References: 39

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.

Fields of Study & Subjects

Machine Learning in Materials Science Big Data and Digital Economy Machine Learning in Bioinformatics

Publication details

Journal / source: The Journal of Physical Chemistry Letters
Publisher: American Chemical Society (ACS)
Volume: 17
Issue: 26
Pages: 7474–7484
Publication date: 18 June 2026
Language: EN

References 39