Application-oriented design of machine learning paradigms for battery science

• Published in: npj computational materials Vol. 11; no. 1; pp. 89 - 18
• Main Author: Wang, Ying
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.04.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/301/1034/1037; 639/301/299/891; Algorithms; Artificial intelligence; Automation; Characterization and Evaluation of Materials; Chemistry and Materials Science; Computational Intelligence; Datasets; Electrolytes; Energy consumption; Learning algorithms; Machine learning; Material properties; Materials Science; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical Modeling and Industrial Mathematics; Neural networks; Optimization; Perspective; Polymers; Science; Simulation; Software engineering; Theoretical
• ISSN: 2057-3960; 2057-3960
• DOI: 10.1038/s41524-025-01575-9

In the development of battery science, machine learning (ML) has been widely employed to predict material properties, monitor morphological variations, learn the underlying physical rules and simplify the material-discovery processes. However, the widespread adoption of ML in battery research has encountered limitations, such as the incomplete and unfocused databases, the low model accuracy and the difficulty in realizing experimental validation. It is significant to construct the dataset containing specific-domain knowledge with suitable ML models for battery research from the application-oriented perspective. We outline five key challenges in the field and highlight potential research directions that can unlock the full potential of ML in advancing battery technologies.