Special Issue

Topic: Machine Learning-assisted Development of Rechargeable Battery Materials

A Special Issue of Journal of Materials Informatics

ISSN 2770-372X (Online)

Submission deadline: 31 Jul 2024

Guest Editor(s)

Prof. Siqi Shi
School of Materials Science and Engineering, Shanghai University, Shanghai, China.
Prof. Lei Zhang
Department of Applied Physics, School of Chemistry and Materials Science, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China.

Special Issue Introduction

In the era of big data and artificial intelligence, the machine learning approach has attracted significant attention in the materials science community. It effectively complements traditional research paradigms such as trial-and-error experiments, theory, and simulation. The rechargeable battery materials, including those for lithium-ion batteries, lithium-metal batteries, lithium-sulfur batteries, lithium-air batteries, and a wide range of alternatives, exhibit complex nature and multidimensional design spaces, making it challenging to identify robust design strategies for materials development. To this end, machine learning methods are considered versatile tools to accelerate the design of rechargeable battery materials. This Special Issue, guest-edited by Lei Zhang (Nanjing University of Information Science and Technology) and Siqi Shi (Shanghai University), will focus on machine learning-assisted development of rechargeable battery materials. Specific areas to be covered in this Special Issue include, but are not limited to:

● Development and application of machine learning algorithms for rechargeable battery material design;

● Materials modeling coupled with machine learning for battery material optimization;

● High-throughput experiments and autonomous laboratories for rechargeable battery materials and devices;

● Interpretable machine learning for understanding underlying mechanisms;

● Development and application of machine learning potentials for battery materials;

● Database construction for battery materials and devices;

● Application of natural language processing, image recognition, and data-driven methods for battery materials and devices; 

● Development of battery software and infrastructure. 

Submission Deadline

31 Jul 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=JMI230925
Submission Deadline: 31 Jul 2024
Contacts: Mengyu Yang, Assistant Editor, JMI@oaepublish.com

Published Articles

Lithium-ion battery health prognosis via electrochemical impedance spectroscopy using CNN-BiLSTM model
Open Access Research Article 26 Jun 2024
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Theoretical design of dendrite-free zinc anode through intrinsic descriptors from symbolic regression
Open Access Research Article 26 May 2024
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Journal of Materials Informatics
ISSN 2770-372X (Online)
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