Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium-Ion Batteries

Area of Science:

• Materials Science
• Electrochemistry
• Computational Chemistry

Background:

• Lithium-ion batteries (LIBs) are crucial for energy storage but face limitations in performance and cost.
• Traditional "trial-and-error" methods for improving LIBs are time-consuming and inefficient.

Purpose of the Study:

• To review artificial intelligence (AI) applications in predicting and discovering battery materials.
• To analyze the use of AI in estimating the state of battery systems.
• To outline challenges and propose a framework for AI in rechargeable LIB development.

Main Methods:

• Review of heterogeneous AI technologies for battery material prediction and discovery.
• Summarization of state-of-the-art machine learning (ML) applications in property prediction (electrolytes, electrodes).
• Analysis of AI for battery state estimation.

Main Results:

• AI significantly accelerates the R&D of novel battery materials and systems.
• Successful examples of AI deployment in battery research are identified.
• Challenges in real-world AI application and potential solutions are discussed.

Conclusions:

• AI offers a powerful approach to overcome limitations in LIB performance and cost.
• An integrated framework is proposed to address challenges in ML for rechargeable LIBs.
• Further development of AI is essential for advancing next-generation battery technologies.