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

June 18, 2026The Journal of Physical Chemistry Letters

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

View Full Paper

Ask AI

Bookmark

Share

Key Points

The research aims to enhance the prediction of Li-ion migration energy barriers in battery cathode materials.
Utilized a convolutional neural network model based on a divide-and-conquer strategy for material screening.
Focused on the synergy of multiple structural descriptors to improve accuracy in predictions.
Adopted hybrid geometric-topological descriptors for modeling.
Demonstrated significant improvement in the prediction accuracy of Li-ion migration energy barriers.
Identified key descriptors that contribute effectively to rapid Li-ion diffusion.
Provided a high-throughput screening method for potential battery materials.

Implication

Study predicts energy barriers for Li-ion migration in battery cathodes, suggesting new screening approaches.

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.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Dai et al. (2026) studied this question.

synapsesocial.com/papers/6a3632a0db0793dc1a539207 https://doi.org/10.1021/acs.jpclett.6c01620

View Full Paper

Ask AI

Bookmark

Share

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading