Skip to main navigation Skip to search Skip to main content

Transformative applications of artificial intelligence in lithium battery materials science: advancements and future prospects

Guangcun Shan*, Zejian Ding, Liujiang Xi, Hongbin Zhao, Jiliang Zhang, Jijian Xu*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

Artificial intelligence (AI) technologies have transformed the field of materials science by enabling efficient data-driven approaches for property prediction and material discovery. Here, we provide an in-depth analysis of AI applications in materials science, focusing on data collection, property prediction, material discovery, and autonomous experimentation. We summarize the primary data sources and increased utility of large language models, which have significantly expedited the material discovery process. Additionally, we examine the application of AI to predict the key properties, emphasizing the transformative role of AI for lithium batteries. Although numerous challenges persist, advancements in AI-driven tools and methodologies provide avenues for accelerating innovation in materials science. © Youke Publishing Co., Ltd. 2025.
Original languageEnglish
Number of pages16
JournalRare Metals
Online published29 Sept 2025
DOIs
Publication statusOnline published - 29 Sept 2025

Funding

The work was financially supported by the National Key R&D Program of China (No. 2022YFB4703400).

Research Keywords

  • Lithium battery
  • Battery materials
  • Large language models (LLM)
  • Machine learning

Fingerprint

Dive into the research topics of 'Transformative applications of artificial intelligence in lithium battery materials science: advancements and future prospects'. Together they form a unique fingerprint.

Cite this