Special Issue

Topic: Machine Learning for Materials Development and State Prediction in Lithium-ion Batteries

A Special Issue of Journal of Materials Informatics

ISSN 2770-372X (Online)

Submission deadline: 31 Dec 2024

Guest Editor(s)

Prof. Fuqian Yang
Materials Engineering Program, Department of Chemical and Materials Engineering, Stanley and Karen Pigman College of Engineering, Lexington, USA.

Guest Editor Assistant(s)

Prof. Yong Li
School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai, China.

Special Issue Introduction

We are pleased to announce the Special Issue on "Machine Learning for Materials Development and State Prediction in Lithium-ion Batteries" in the Journal of Materials Informatics (JMI, Online ISSN 2770-372X). This issue will be overseen by Professor Tong-Yi Zhang as Editor-in-Chief and Professor Xingjun Liu as Executive Editor, with a distinguished Editorial Board including Professor Tong-Yi Zhang, Professor Xingjun Liu, Professor Huajian Gao, Professor Gerbrand Ceder, Professor Li-Quan Chen, and Professor Dongying Ju.

This Special Issue will highlight cutting-edge research that applies machine learning (ML) methodologies to critical challenges in developing lithium-ion battery (LIB) materials and monitoring LIB conditions. It will showcase the latest advancements in predictive modeling, data-driven optimization, and real-time monitoring of LIBs. Contributions will come from materials science, electrochemistry, data science, and ML, focusing on novel ML algorithms, computational models, and experimental techniques that enhance battery materials and provide accurate battery health and lifespan predictions under various operating conditions.

Specific topics of interest include, but are not limited to, the following:
● Application of ML algorithms to discover new LIB materials with improved properties;
● Development of ML models to predict the capacity, efficiency, and cycle life of LIBs;
● Utilization of ML for early detection of performance degradation and fault diagnosis;
● Advanced ML approaches for real-time estimation of State of Charge (SOC) and State of Health (SOH) in LIBs;
● Implementation of ML in developing intelligent battery management systems (BMS) for improved efficiency and safety;
● Use of big data analytics for processing and interpreting large datasets from LIB experiments and simulations;
● Combining experimental data with computational ML models for better accuracy and validation;
● Application of ML to model and predict diffusion-induced stress or plastic deformation in LIB electrodes.

We anticipate receiving your contributions to this exciting Special Issue.


Guest Editors
Prof. Dr. Fuqian Yang

Prof. Dr. Yong Li


Machine learning, materials development, state prediction, computational methods, diffusion-induced stress

Submission Deadline

31 Dec 2024

Submission Information

For Author Instructions, please refer to https://www.oaepublish.com/jmi/author_instructions
For Online Submission, please login at https://oaemesas.com/login?JournalId=JMI&SpecialIssueId=JMI240523
Submission Deadline: 31 Dec 2024
Contacts: Linda Cui, Assistant Editor, Editor@jmijournal.com

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Journal of Materials Informatics
ISSN 2770-372X (Online)
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