Machine Learning Boosting the Development of Advanced Lithium Batteries.

Area of Science:

• Materials Science
• Electrochemistry
• Computer Science

Background:

• Lithium batteries (LBs) are critical for portable electronics, electric vehicles, and smart grids, facing high performance demands.
• Developing advanced LBs requires efficient methods for material discovery and performance prediction.
• Machine learning (ML) offers a powerful approach to accelerate these development processes.

Purpose of the Study:

• To provide an overview of ML procedures and methods relevant to lithium battery research.
• To highlight successful applications of ML in the development of advanced LBs.
• To discuss current challenges and future perspectives for ML in the LB field.

Main Methods:

• Review of existing literature on ML applications in lithium battery development.
• Introduction to fundamental ML concepts and representative techniques.
• Analysis of case studies demonstrating ML's impact on material discovery and performance prediction.

Main Results:

• ML has demonstrated significant success in accelerating the discovery of novel materials for LBs.
• ML models can accurately predict LB material properties, reducing experimental costs and time.
• The review identifies key areas where ML has already enhanced LB development.

Conclusions:

• ML is a transformative tool for advancing lithium battery technology.
• Further research and adoption of ML are crucial for overcoming current challenges in LB development.
• This review aims to foster greater understanding and application of ML in the field of advanced LBs.