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Related Concept Videos

The Electrical Double Layer01:30

The Electrical Double Layer

In the region where two bulk phases meet, an intricate electric charge distribution arises due to charge transfer, ion adsorption, molecular orientation, and charge distortion. This complex distribution is commonly referred to as the electrical double layer.When a solid electrode interfaces with ions in an electrolyte solution, the speed of electron transfer dictates the rates of oxidation and reduction. The electrode acquires a charge through the escape of atoms into the solution as cations or...
Transport Number01:31

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The transport number is the fraction of the total current carried by an ion in an electrolyte solution. It is defined as the ratio of the current carried by a specific ion to the total current flowing through the solution. The transport number, t, is central to understanding ionic mobility, which describes how fast an ion moves under the influence of an electric field. This link connects the physical behavior of ions in solution to the chemical processes that occur during electrochemical...

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Related Experiment Video

Updated: Jun 19, 2026

Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques
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Predicting Li-Ion Migration Energy Barriers in Battery Cathode Materials via Convolutional Neural Network Model Based

Jingwen Dai1, Zhiyan Xue2, Yan Tian1

  • 1State Key Laboratory of Materials for Advanced Nuclear Energy & Institute for Sustainable Energy, Shanghai University, Shanghai 200444, China.

The Journal of Physical Chemistry Letters
|June 18, 2026
PubMed
Summary

Machine learning accurately predicts lithium-ion (Li-ion) migration energy barriers in battery materials using a novel convolutional neural network (CNN) model. This approach enhances material screening for faster Li-ion diffusion.

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Published on: March 7, 2022

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning

Background:

  • Accurate prediction of lithium-ion (Li-ion) migration energy barriers is crucial for developing advanced battery cathode materials.
  • Existing machine learning models face challenges in capturing complex material features and integrating cross-scale information.
  • Developing efficient structural descriptors and neural network architectures is essential for accurate prediction.

Purpose of the Study:

  • To propose a novel convolutional neural network (CNN) model for efficient prediction of Li-ion migration energy barriers.
  • To develop and utilize hybrid geometric-topological descriptors for enhanced feature representation.
  • To provide an accurate and efficient method for high-throughput screening of battery materials.

Main Methods:

  • Development of a CNN model incorporating hybrid geometric-topological descriptors.
  • Geometric descriptors capture local Li-O polyhedra; topological descriptors characterize migration pathway connectivity using persistent homology.
  • Model optimization using residual blocks and L2 regularization, compared against Recurrent Neural Network (RNN) and Fourier-Feature Network (FFN) models.

Main Results:

  • The optimized CNN model achieved a mean absolute error (MAE) of 0.0589 eV, outperforming RNN and FFN models.
  • The minimum Li-O distance was identified as the most critical factor influencing the migration barrier.
  • Hybrid descriptors showed synergistic effects, emphasizing the necessity of combining geometric and topological information.

Conclusions:

  • The proposed CNN model with hybrid descriptors offers an efficient and accurate approach for predicting Li-ion migration energy barriers.
  • This method facilitates high-throughput screening of cathode materials with desirable Li-ion diffusion properties.
  • The study highlights the importance of integrated structural descriptors for advancing battery material discovery.