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Generative Deep Learning for Advanced Battery Materials
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DOI：10.1002/batt.202500494.png)
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Abstract
En 中文
The development of battery materials presents a complex multiscale challenge, where optimizing the properties of battery systems across various length scales is essential for achieving targeted performance, enhanced safety, lower costs, and resource availability. Traditional methods for solving this complex, multiscale problem rely on time-intensive trial-and-error approaches, which hinder progress. However, integrating advanced machine learning (ML) frameworks significantly changes the landscape of battery materials research by enabling faster discovery, predictive modeling, and optimization of material properties. Among the ML frameworks, generative deep learning (DL) models stand out, as they capture the statistics of real-world scenarios by learning an underlying condensed representation of a higher-dimensional input space to generate information-rich outputs. By merging computational techniques with experimental research, generative DL provides a significant paradigm shift in analyzing battery materials. This review aims to provide valuable insights into generative models, highlighting their potential to accelerate the characterization, screening, and design of battery materials.
Keywords:
batteries
deep learning
generative modeling
materials design
machine learning
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