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

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.

Sincerely,

Guest Editors
Prof. Dr. Fuqian Yang

Prof. Dr. Yong Li

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

31 Dec 2024